“Identifying Value Co-creation in Innovation Ecosystems Using Social Network Analysis,” Inaugural Lecture: Innovation Forum. Hong Kong University of Science and Technology. August 2, 2010.
22. “没有一种数据可以与拥有更多的数据媲美” (Mercer at Arden. House, 1985) “There is no data like more data” (Mercer at Arden. House, 1985) Tan, Steinbach, Kumar; 2004 2,000 个点 500 个点 8,000 个点
26. . 创新生态系统的数据库 35,000 companies include: Sectors: Advertising, biotech, cleantech, consulting, ecommerce, enterprise, games_video, hardware, legal, mobile, network_hosting, public relations, search, security, semiconductor, software, web, other firms serving these. Investment profiles from Ltd to public, financing rounds identified Merger & Acquisition profiles Neil Rubens, Kaisa Still, Jukka Huhtamaki, Martha G. Russell “Leveraging Social Media for Analysis of Innovation Players and Their Moves” Technical Report. Media X, Stanford University, Feb.2010.
30. 亟待更新 区域科技产业经济发展 “全球的商业地图被越来越多的区域集中化的公司群体，其相关的经济人和机构所占据。” The Use of Data and Analysis as a tool for cluster policy, Green Paper on international best practices and perspectives prepared for the European Commission, November 2008 “有时一个产业群体中的成员分布于全球不同区域，但他们可以通过信息和通讯技术联系在一起... 所以人们会用“e-群体“去形容它们” Danese, Filippini, Romano, Vinelli 2009 “科技化的趋势正在带动发达市场经济中产生更多的创新。”Baldwin & von Hippel November 2009, Harvard Business School Working Paper 10-038 “各地的政府部门在积极地采取措施，加强国家的创新体系。因为他们都意识到要想成为经济发展的领军者及加强国际竞争力，创新能力和商业化高科技产品的能力发挥着日益重要的作用。”Understanding Research, Science and Technology Parks: Global Best Practices, National Research Council of the National Academies, Report 2009
31. . Neil Rubens, Kaisa Still, Jukka Huhtamaki, Martha G. Russell “Leveraging Social Media for Analysis of Innovation Players and Their Moves” Technical Report. Media X, Stanford University, Feb.2010.
32. # 公司数 # 人数 Neil Rubens, Kaisa Still, Jukka Huhtamaki, Martha G. Russell “Leveraging Social Media for Analysis of Innovation Players and Their Moves” Technical Report. Media X, Stanford University, Feb.2010.
33. . 美国科技公司的数量 按行业划分，2009年12月 Neil Rubens, Kaisa Still, Jukka Huhtamaki, Martha G. Russell “Leveraging Social Media for Analysis of Innovation Players and Their Moves” Technical Report. Media X, Stanford University, Feb.2010.
34. . 美国科技公司的数量 广告和网络，2009年12月 Neil Rubens, Kaisa Still, Jukka Huhtamaki, Martha G. Russell “Behind the Innovation Curtain: Mobile Players and Their Moves.” Submitted to the International Conference on Mobile Business,” Intl Conf on Mobile Business. Neil Rubens, Kaisa Still, Jukka Huhtamaki, Martha G. Russell “Leveraging Social Media for Analysis of Innovation Players and Their Moves” Technical Report. Media X, Stanford University, Feb.2010.
35. . 美国科技公司的数量 清洁科技和生物科技，2009年12月 Neil Rubens, Kaisa Still, Jukka Huhtamaki, Martha G. Russell “Behind the Innovation Curtain: Mobile Players and Their Moves.” Submitted to the International Conference on Mobile Business,” Intl Conf on Mobile Business. Neil Rubens, Kaisa Still, Jukka Huhtamaki, Martha G. Russell “Leveraging Social Media for Analysis of Innovation Players and Their Moves” Technical Report. Media X, Stanford University, Feb.2010.
36. . 科技公司的数量 硅谷, 按行业划分, 2009年12月 Neil Rubens, Kaisa Still, Jukka Huhtamaki, Martha G. Russell “Behind the Innovation Curtain: Mobile Players and Their Moves.” Submitted to the International Conference on Mobile Business,” Intl Conf on Mobile Business. Neil Rubens, Kaisa Still, Jukka Huhtamaki, Martha G. Russell “Leveraging Social Media for Analysis of Innovation Players and Their Moves” Technical Report. Media X, Stanford University, Feb.2010.
37. . 科技公司的数量 西雅图, 按行业划分, 2009年12月 Neil Rubens, Kaisa Still, Jukka Huhtamaki, Martha G. Russell “Behind the Innovation Curtain: Mobile Players and Their Moves.” Submitted to the International Conference on Mobile Business,” Intl Conf on Mobile Business. Neil Rubens, Kaisa Still, Jukka Huhtamaki, Martha G. Russell “Leveraging Social Media for Analysis of Innovation Players and Their Moves” Technical Report. Media X, Stanford University, Feb.2010.
38. . 科技公司的数量 华盛顿特区, 按行业划分, 2009年12月 Neil Rubens, Kaisa Still, Jukka Huhtamaki, Martha G. Russell “Behind the Innovation Curtain: Mobile Players and Their Moves.” Submitted to the International Conference on Mobile Business,” Intl Conf on Mobile Business. Neil Rubens, Kaisa Still, Jukka Huhtamaki, Martha G. Russell “Leveraging Social Media for Analysis of Innovation Players and Their Moves” Technical Report. Media X, Stanford University, Feb.2010.
39. . 科技公司的数量 纽约, 按行业划分, 2009年12月 Neil Rubens, Kaisa Still, Jukka Huhtamaki, Martha G. Russell “Behind the Innovation Curtain: Mobile Players and Their Moves.” Submitted to the International Conference on Mobile Business,” Intl Conf on Mobile Business. Neil Rubens, Kaisa Still, Jukka Huhtamaki, Martha G. Russell “Leveraging Social Media for Analysis of Innovation Players and Their Moves” Technical Report. Media X, Stanford University, Feb.2010.
40. 纽约 硅谷 Neil Rubens, Kaisa Still, Jukka Huhtamaki, Martha G. Russell “Behind the Innovation Curtain: Mobile Players and Their Moves.” Submitted to the International Conference on Mobile Business,” Intl Conf on Mobile Business. 波士顿 西雅图 Neil Rubens, Kaisa Still, Jukka Huhtamaki, Martha G. Russell “Leveraging Social Media for Analysis of Innovation Players and Their Moves” Technical Report. Media X, Stanford University, Feb.2010.
43. 清洁技术 Kaisa Still, Neil Rubens, JukkaHuhtamäki, and Martha G. Russell , “Networks of Executive Women in Technology-Based Innovation Ecosystems,” Technical Report , Media X, Stanford University, May.2010.
44. 生物技术 Kaisa Still, Neil Rubens, JukkaHuhtamäki, and Martha G. Russell , “Networks of Executive Women in Technology-Based Innovation Ecosystems,” Technical Report , Media X, Stanford University, May.2010.
45. 公关 Kaisa Still, Neil Rubens, JukkaHuhtamäki, and Martha G. Russell , “Networks of Executive Women in Technology-Based Innovation Ecosystems,” Technical Report , Media X, Stanford University, May.2010.
46. 网络 Kaisa Still, Neil Rubens, JukkaHuhtamäki, and Martha G. Russell , “Networks of Executive Women in Technology-Based Innovation Ecosystems,” Technical Report , Media X, Stanford University, May.2010.
47. 角色 首席技术官 投资者 首席市场官 创始人 Kaisa Still, Neil Rubens, JukkaHuhtamäki, and Martha G. Russell , “Networks of Executive Women in Technology-Based Innovation Ecosystems,” Technical Report , Media X, Stanford University, May.2010.
58. 创新生态系统体制 Applied Research Initiative on Data-driven Visualization of Innovation Ecosystems for Local and Global Innovation Accelerators Neil Rubens, neil@hrstc.org Jukka Huhtamäki, jukka.huhtamaki@tut.fi Kaisa Still, kaisastill@yahoo.com Martha Russell, martha.russell@stanford.edu 数据和分析 前提 结成联盟是能促进成功的. 成功的因素是可识别的. 分析和比较不同国家形成国际联盟的过程和由此产生的结果。 [美国, 中国, 日本, 芬兰, 等等] 国家数据库中的公司，人员，资源流动F和交易。 网络分析,确立模式，利益相关者的访问. 数据伙伴，分析伙伴, 及社区实践者的伙伴。 信息传播 FTF 和虚拟空间. 目标 已建立的体制 新的体制 [Deighton, Quelch, 2009] 1990 2000 1980 政府 行业 学院 Triple Helix [Russell 2008] [Smith, Powell, 2004] [Tekes]
Notas do Editor
Please think of several patterns and outliers in bicicles picture.ASK AUDIENCE---So let me just mention a few:Color is one of the patters that jumps out right awayFor example there is a lot of aluminum colorsYellow bike jumps out as an outlierIf we look closer we may also notice that there is only one bike where the handles are greenOnly a few bikes have their seat covered with plasticBikes are more or less lined upThere is a bike that is facing the wrong way though----------Even in these small dataset there are so many patterns and outliersBut how many of them are interesting; that really depends.We try to find patterns that are novel; since telling people that bicycles tend to have two wheels is perhaps not so interesting.What is interesting also depends on the purpose;A person checking whether bicycles have permit for parking – is looking for a specific outliersWhen I look for my own bike; I have a different outlier in mindSo ability to spot things that are interesting is extremely important.Outliers are normally discarded in data mining …Because you are often trying to find a pattern, and outliers screw up things.In business, some outliers have become very successful as described in the following book.So we thing it is interesting to look not only for patterns but also for outliers
Can’t do data mining without the data; so we need data and the more the better – since then we can see patterns more clearly
Also when we have more dimensions it is easier to spot patterns
My name is Neil Rubens, I am not a journalist; I am a data miner – but I think in essense it is not so different.
It is rare that the data is simply brought to us on a silver platterWe have to try hard to actively acquire it
Now let me briefly describe a case of how we utilized the above mentioned principles.In our project we try to understand innovation, so have gathered the data on companies, people and money.What makes this data set different, besides its timeliness is the majority of data (thanks to social media) is about small companies having between 1 – 5 employees.A lot of innovation happens there so it is important to track.
This shows how the models of innovations have evolved reflecting the changes
This shows how we have evolved from the local/regional activities
At the core of this research we have what initially were called “regional technology-based economic development”– however each of the three parts has experienced changes, which calls for updating the whole concept
This map indicates the location of the companies. Size of circle indicates number of companies.For this part of analysis we have used Tableau Software.
We can also look at the companies by sector
We can try to analyze relations between sectors; here are the advertising and web sectorsA lot of things going on in Silicon Vaelly; but also in the North East and other parts
Here is the biotech and cleantech
We can also at specific cities and regionsSV looks very interesting
This is seattle
DC
And NY
So as you can see the patters are very different from city to city
So far I have shown analysis based on the spatial distance;However the aspects of distance is changing;We don’t know where these people are physically located but they seem to be in the same space.
So the new maps may be based on the connections; rather than on distance.For this analysis we have utilized an open source tool called NodeXL
-------------------------http://www.bbsservicesinc.com/sitebuildercontent/sitebuilderpictures/world-map.gifPartners: Government agenciesEducational institutionsSME’s Services & consultanciesVenture groupsLarge organizations Data points:PatentsLicensesJobsPublicationsCitationsResource flows – investments, sales, valuations-----------------ChinaJapan – JSTNYC – NYC MediaLabAustin – MCC, SematechMpls/St.P – Finland – TEKKES, FINNODEAbuDhabi
