Previous studies related to open innovation have presented piecewise implications in relation to various knowledge management capacities. The study published by Lichtenthaler and Lichtenthaler in 2009 presented a model that combines the various open innovation capacities of firms in view of a mix of knowledge management, dynamic capability and absorptive capacity. Despite these efforts, there have been few empirical studies on the relationships among capacities, or between capacities and performance from an integrated perspective. Therefore, this study seeks to clarify the relationships among knowledge capacities and between knowledge capacities, technological innovation and financial performance at the firm level. Our findings are that the transformative, connective, inventive and absorptive capacities both directly and indirectly affects technological innovation performance; and innovative and desorptive capacities are the key factors connecting technological innovation to financial performance. This study provides managerial implications for the balanced development of the various knowledge capacities and the improvement of technological innovation and financial performance for firm knowledge managers.

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Purpose: 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?" Design/methodology/approach: 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. Findings: 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.

Responding to the lack of empirical research on the effect of collective intelligence on open innovation in the fourth industrial revolution, we examined the relationship between collective intelligence and open innovation. Collective intelligence or crowd innovation not only produces creative ideas or inventions, but also moderates any firm to innovate inside-out, outside-in, or in a coupled manner. We asked the following research questions: Does collective intelligence (or crowd innovation) motivate open innovation? Is there any difference in the effect of collective intelligence on open innovation by industry? These research questions led to the following three hypotheses: (1) Collective intelligence increases the performance of a firm, (2) collective intelligence will moderate the effect of open innovation, and (3) differences exist between the automotive industry and the pharmaceutical industry in these two effects. To empirically examine these three hypotheses, we analyzed the registered patents of these two industries from 2000 to 2014 over a 15-year period. These automotive and pharmaceutical patents were registered in the B60 category and the A61K category of the Korea Patent office, respectively. Collective intelligence was measured by co-invention. We found differences in the effects of collective intelligence on open innovation between the two industries. In the automotive industry, collective intelligence not only directly increased the performance, but also indirectly moderated the open innovation effect. However, this was not the case for the pharmaceutical industry.

Abstract:The purpose of this study is to address the following research question: What is the relationship between open innovation and firm performance? The study built up a research framework with three factors-i.e., open innovation strategy, time scope, and industry condition-to find out the concrete open innovation effects on firm performance. This study adopted four different research methods. Firstly, we applied the aforementioned factors to a game of life simulation in order to identify the concrete differences of open innovation effects on firm performance. Secondly, the study examined the real dynamics of open innovation effects on firm performance in the aircraft industry-one of the oldest modern industries-through a quantitative patent analysis. It then looked into the effects of major factors that impact open innovation effects. Thirdly, this study developed a mathematical model and tried to open the black box of open innovation effects on firm performance. Lastly, the study logically compiled research on open innovation effects on firm performance through the presentation of a causal loop model and derived the possible implications.

Abstract:The main topic of this paper is the effects of distance between technology and the market, on open innovation. For this, we set up two research questions, as follows: Is there any relation between the distance between technology and the market, and open innovation? If there is, what differences are there in the relation among Fortune 500 companies, non-Fortune 500 companies, laboratories, universities, and start-ups? First, this study measured the distance between technology and the market of a patent by the size of its list of references and citations. Second, the OI network among patent application subjects was described based on patent similarity. Third and most importantly, regression analyses were used to answer the research questions. The first result was that there were differences in the distance and OI among Fortune 500 firms, Fortune non-500 firms, laboratories, universities, and start-ups. Thus, there are relations between the distance between technology and the market, and open innovation. The second result was that the distance between technology and the market was found to moderate the open innovation effect in Fortune 500 companies and laboratories.

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