Knowledge combination modeling: The measurement of knowledge similarity between different technological domains
Introduction
It is said that innovation comes from a recombination of knowledge (Dosi, 1982, Nelson and Winter, 1977, Schumpeter, 1934) and that combining one's own knowledge with that of different industry and different technology domains has the possibility of bringing new knowledge creation (Schoenmakers and Duijsters, 2010, Gassmann and Zeschky, 2008, Dosi, 1982). On the other hand, the technological domain of industries with a complex system has a wide range of technological sub-domains (Fleming and Sorenson, 2001, Eriksson, 2000). It is not easy for engineers of an industry to search for a new candidate of knowledge combination in knowledge of another industry with a complex system; firstly they should need to identify the technological sub-domains of the other, and then, to select sub-domains to look further. The information of sub-domains also must be updated frequently (Herrero et al., 2010). The cost of the collection and the integration of different knowledge (Nakamura et al., 2011a, Kajikawa et al., 2006, Tijssen, 1992) and the uncertainty of success (Schilling and Green, 2011, Moorthy and Polley, 2010) are problems that limit practitioners to explore new opportunities.
To support practitioners to have such exploration, namely breadth activities, and to bring innovation, the authors focus on one side of breadth activities; namely, searching the technological knowledge of other industries and integrating it to their own knowledge. We propose a knowledge combination model and discuss methodologies that effectively identify a technological sub-domain that can be combined between the two industries.
We focus on patents as the information source of technological knowledge of focused industries because firstly patent is considered to be the best available indicator for R&D invention related to technology and outcomes of innovation activities (OECD, 1994). And secondly we believed that there is a potential need in practitioners for scientometrics that can support breadth search with patents. Practitioners contacted in this study explained that, although a patent is essential to protect practitioners' intellectual products and investigate competitors' strategy, difficulty in searching information from patent data hinders frequent use of patents as a source of knowledge. And computer-based bibliometrics approaches are taken because it can process vast amount of data and it is expected to ease breadth search (Alavi and Leidner, 2001, Smallheiser and Swanson, 1998, Smalheiser, 2012, Herrero et al., 2010, Cantu and Ceballos, 2010, Fleming and Sorenson, 2001, Kostoff, 2008).
This paper is organized as follows: The next section reviews previous literature. The third section proposes a knowledge combination model and three measurements. The fourth section conducts a case study on technological knowledge of automobile and aeronautic industry and shows identified technological domains and highlighted pairs of technological domains. The section also discusses the results with automobile and aeronautic experts. The Discussion section compares the three methodologies. The final section concludes this paper with the findings.
Section snippets
Literature
Patent is often used in the innovation literatures. For example, patent is used as the indicators of technological knowledge of focused industries in the literatures mapping technological portfolios of a company or an industry. Leydesdorff et al. (2012) discussed methodologies to map the technological portfolios and the relation between the identified technology using International Patent Classification (IPC) and patent citation analysis approach. Schoen et al. (2012) discussed methodologies to
Depth and breadth knowledge combination model
The methodology proposed in this paper aims to support the following knowledge recombination process of practitioners, that is, identifying the technological sub-domains of other industries, selecting sub-domains to combine and researching for bringing new knowledge. And to do that, firstly we propose a knowledge combination model between two technological domains, named the DB-Combination model (Fig. 1). We assume that, limited to the technological knowledge and the combination between
Technological sub-domain identification
Among the three approaches (Table 1), the A1 and A2 approaches used citation analysis to identify technological domains from the data. The patent dataset of the automobile and aircraft industries was analyzed separately in the A1 research, and was combined in the A2 research.
In the A1 research, the MC of automobile and aircraft consists of 60,458 patents and 8281 patents, respectively, and were divided into 303 and 104 clusters. The number of patents in a cluster, that is, the size of clusters,
Discussion
Table 9 compares A1, A2 and A3 results. For approaches of technological sub-domain identification, the citation analysis approach limited the identification to the MC so that the coverage of data was much inferior to the IPC approach. If a comprehensive overview of technological sub-domains in other industries is needed, the IPC approach can satisfy the needs as a list of technological knowledge. Moreover, the overview of the IPC approach can be obtained easily with spread sheet software. On
Summary
To support practitioners to collect breadth technological knowledge in other industries and to combine it to their own knowledge, this paper proposed the DB-Combination model that considered the similarities of two technological knowledge domains and three different knowledge combinations in depth and breadth. We also investigated three methodologies A1, A2 and A3 to highlight the three combination pairs from patents by identifying technological knowledge sub-domains and measuring their
Acknowledgment
A part of this research is financially supported by theResearch Institute of Science and Technology for Society (RISTEX) of Japan Science and Technology Agency (JST).
Dr. Hiroko Nakamura is a Project Researcher at the Centre for Aviation Innovation Research and also at the Global Leader Program for Social Design and Management at the University of Tokyo. She previously worked for Nissan Motor Co. Ltd., as a product planner. She received her bachelor's, and master's degree, and Ph.D. from the same university and a Special Master's degree from the Ecole Centrale Paris. Her research interests include development of methodology for technology and innovation
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Dr. Hiroko Nakamura is a Project Researcher at the Centre for Aviation Innovation Research and also at the Global Leader Program for Social Design and Management at the University of Tokyo. She previously worked for Nissan Motor Co. Ltd., as a product planner. She received her bachelor's, and master's degree, and Ph.D. from the same university and a Special Master's degree from the Ecole Centrale Paris. Her research interests include development of methodology for technology and innovation management.
Dr. Shinji Suzuki is a Professor at the Department of Aeronautics and Astronautics and the Director of the Center for Aviation Innovation Research at the University of Tokyo. He received his Ph.D. degree in Engineering from the University of Tokyo in 1986, after his research career at Toyota's Central R&D Labs, Inc., in Japan. He received his bachelor's, and master's degrees from the same university. His main research interests are in the design and control aspects of air safety and unmanned aerial vehicles. He is currently an Executive Committee Member of ICAS.
Dr. Ichiro Sakata is a Professor at the Department of Technology Management for Innovation, the Director of Innovation Policy Research Center and the co-head of Presidential Endowed Chair for Electricity Network Innovation by Digital Grid at the University of Tokyo. He received his bachelor's degree and Ph.D. from the same university, and master's degree from Brandeis University. He has 20 years'of working experience at the Japanese Ministry of Economy, Trade and Industry (METI). His research interests include technology management, technology roadmap and innovation network. He has published more than 100 papers in peer reviewed journals and conference proceedings.
Dr. Yuya Kajikawa is an Associate Professor at Graduate School of Innovation Management, Tokyo Institute of Technology. He is also a visiting researcher at Graduate School of Engineering, The University of Tokyo. He received his bachelor's, and master's degrees, and Ph.D. from the University of Tokyo. His research interests include development of methodology for technology and innovation management. He has a number of publications in peer-reviewed journals and conference proceedings, which cover a variety of disciplines including engineering, information science, environmental science, and technology and innovation management. He serves as an Associate Editor of Technological Forecasting and Social Change.
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