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- W2595008242 abstract "It is critical for “catching-up” countries to narrow innovation gaps with developed countries by developing emerging industries. This research introduces a data-mining based method to systematically assess the national innovation gap that is specifically for emerging industries. The method examines the five key attributes of emerging industries, including the ownership of platform technologies, globalization intention, international knowledge position, university-industry linkage, and cross-disciplinary technology development. In particular, this method combines data-mining with experts' knowledge to build patent-training examples, and then uses a support vector machine-based classifier to single out all high-quality patents for each innovation attribute. Based on the selected high-quality patents, the authors utilize a factorial design analysis to systematically evaluate the innovation gap between countries. This method can significantly reduce measurement bias of traditional single patent indicators. In addition, it also can robustly adjust measuring weights in response to the specifics of each innovation attribute, while traditional multi-attribute evaluation methods cannot. As a result, this research empirically shows that China' industrial robot sector has apparent innovation gaps compared to developed economies, specifically in university-industry linkage, cross-disciplinary competence, and globalization intention, and this calls for the attention of policy makers and industrial experts." @default.
- W2595008242 created "2017-03-23" @default.
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- W2595008242 date "2017-06-01" @default.
- W2595008242 modified "2023-10-17" @default.
- W2595008242 title "Using the data mining method to assess the innovation gap: A case of industrial robotics in a catching-up country" @default.
- W2595008242 cites W1035961460 @default.
- W2595008242 cites W179677862 @default.
- W2595008242 cites W1966957980 @default.
- W2595008242 cites W1968626469 @default.
- W2595008242 cites W1969145652 @default.
- W2595008242 cites W1970510654 @default.
- W2595008242 cites W1975212776 @default.
- W2595008242 cites W1975600491 @default.
- W2595008242 cites W1977547494 @default.
- W2595008242 cites W1978467004 @default.
- W2595008242 cites W1981123236 @default.
- W2595008242 cites W1985723194 @default.
- W2595008242 cites W1987090983 @default.
- W2595008242 cites W1989209505 @default.
- W2595008242 cites W1991711794 @default.
- W2595008242 cites W1994356057 @default.
- W2595008242 cites W1997168127 @default.
- W2595008242 cites W1997638227 @default.
- W2595008242 cites W2000039169 @default.
- W2595008242 cites W2002569379 @default.
- W2595008242 cites W2005169389 @default.
- W2595008242 cites W2010004560 @default.
- W2595008242 cites W2010612387 @default.
- W2595008242 cites W2010673439 @default.
- W2595008242 cites W2014143835 @default.
- W2595008242 cites W2018678104 @default.
- W2595008242 cites W2020362991 @default.
- W2595008242 cites W2024232046 @default.
- W2595008242 cites W2033604437 @default.
- W2595008242 cites W2033745439 @default.
- W2595008242 cites W2033844804 @default.
- W2595008242 cites W2040622673 @default.
- W2595008242 cites W2041558268 @default.
- W2595008242 cites W2042459243 @default.
- W2595008242 cites W2044047874 @default.
- W2595008242 cites W2045443856 @default.
- W2595008242 cites W2052709366 @default.
- W2595008242 cites W2053900390 @default.
- W2595008242 cites W2055655842 @default.
- W2595008242 cites W2056909456 @default.
- W2595008242 cites W2064383926 @default.
- W2595008242 cites W2072227994 @default.
- W2595008242 cites W2072643952 @default.
- W2595008242 cites W2077079584 @default.
- W2595008242 cites W2078257494 @default.
- W2595008242 cites W2079063728 @default.
- W2595008242 cites W2082790601 @default.
- W2595008242 cites W2082842666 @default.
- W2595008242 cites W2083776263 @default.
- W2595008242 cites W2084565607 @default.
- W2595008242 cites W2090728293 @default.
- W2595008242 cites W2101988552 @default.
- W2595008242 cites W2113581304 @default.
- W2595008242 cites W2115173056 @default.
- W2595008242 cites W2115768891 @default.
- W2595008242 cites W2118575013 @default.
- W2595008242 cites W2120070566 @default.
- W2595008242 cites W2136968651 @default.
- W2595008242 cites W2157975090 @default.
- W2595008242 cites W2159901154 @default.
- W2595008242 cites W2165429918 @default.
- W2595008242 cites W2179442796 @default.
- W2595008242 cites W2192305995 @default.
- W2595008242 cites W2195756904 @default.
- W2595008242 cites W2201635852 @default.
- W2595008242 cites W2204751822 @default.
- W2595008242 cites W2270783961 @default.
- W2595008242 cites W2317145280 @default.
- W2595008242 cites W2514773516 @default.
- W2595008242 cites W3124680412 @default.
- W2595008242 cites W3142518765 @default.
- W2595008242 cites W3161759735 @default.
- W2595008242 cites W4232861544 @default.
- W2595008242 cites W4239510810 @default.
- W2595008242 cites W4251841182 @default.
- W2595008242 cites W635628814 @default.
- W2595008242 cites W2137810110 @default.
- W2595008242 doi "https://doi.org/10.1016/j.techfore.2017.02.035" @default.
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