Value Co-Creation in Innovation Ecosystems (Chinese)
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Innovation Ecosystems presentation to Hong Kong University of Science and Technology (HKUST) at Innovation Forum hosted Prof. Yan Xu. Aug.2010
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Value Co-Creation in Innovation Ecosystems (Chinese)
- 1. 使用社交网络分析在创新生态系统中协同创造价值创新生态系统网络Martha G Russell, Neil Rubens
- 9. Sr. Research Scholar, HSTAR Institute
- 10. Associate Director, Media X at Stanford University
- 11. Neil Rubens, PhD, neil@hrstc.org
- 12. Assistant Professor, Graduate School of Information Systems
- 13. University of Electro-Communications, Tokyo
- 16. Hypermedia Laboratory (HLab) of Tampere University of Technology (TUT).
- 17. Kaisa Still, PhD, kaisastill@yahoo.com
- 19. Beijing DT Electronic Technology Co., Ltd
- 21. Graduate student, Texas Advertising, UT Austin
- 23. Jiafeng (Camilla) Yu, camillayu@gmail.com
- 24. M.A. in Advertising in Planning Track
- 27. “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
- 28. 更多的维度：一把双刃剑 对数据的需求量增多 http://wissrech.ins.uni-bonn.de/research/projects/engel/engelpr2/pr2_thumb.jpg 能较容易地找出模式 http://www.iro.umontreal.ca/~bengioy/yoshua_en/research_files/CurseDimensionality.jpg
- 29. Have to react QUICKLY http://www.flickr.com/photos/clydeorama/3495284608/
- 30. Traditional Data Gathering Methods http://www.flickr.com/photos/tomatoskin/1339929731/
- 31. News Organizations Social Media Federation WILLE Framework Active Intelligence Analysis Mining Visualization Private Data
- 32. . 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.
- 33. # 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.
- 34. 创新的地点 从地方到区域到虚拟共享空间 Innovation Acceleration Networks ?
- 36. . 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.
- 37. 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
- 38. 距离 过去 现在
- 39. 新型的组织架构图
- 40. Relationship Interlocks Executives and key employees Transfer of technologies and knowledge, professional networks, business culture, value-chain resources Directors US Fortune 500 firms interlocked (shared directors) with average 7 other firms Corporate governance embedded and filtered through social structures Executive compensation, strategies for takeovers, defending against takeovers Gerald F. Davis, “The Significance of Board Interlocks for Corporate Governance,” Corporate Governance 4:3, 1996 Investors and service providers Awareness of external forces, competitive insights, resource leverage Relationship interlocks provide Social relationship “filter” for governance, information flow & norms Transfer of implicit and explicit know-how Mental models http://fusionenterprises.ca/Business_Training.php
- 42. 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.
- 43. 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.
- 44. 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.
- 45. 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.
- 46. 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.
- 48. How are these patterns similar or different to those made by the rest of the world into China?http://4.bp.blogspot.com/_qFju91K89HM/SxRpABd1DTI/AAAAAAAABjw/6LaSJfjfk-I/s1600/Unexpected_Guests.jpg http://successbeginstoday.org/wordpress/wp-content/unexpected2.jpg
- 51. 54亿美元的投资进入中国
- 52. 31亿美元的投资来自中国洞察: 进出中国的金融资源的流动 更多说明性而非描述性／观点性的结果 NodeXL, Tableau Innovation Ecosystem Network
- 53. 初步数据分析: 53% (113) 的中国企业属于eCIS行业 50 % (66) 的外商公司属于eCIS行业 进一步洞察: 流入和流出中国的投资模式及其不同的特点 具体: eCIS行业环境电子商务和电子安全系统=电子商务, 软件研究, 网络存取, 移动, 游戏和录影, 企业 Innovation Ecosystem Network
- 54. 快速收获来自中国的投资(进行投资) Innovation Ecosystem Network
- 55. 慢慢耕耘进入中国的投资(获得投资) Innovation Ecosystem Network
- 56. 网络标准 Innovation Ecosystem Network
- 57. 投资公司连接起的新兴中国商业群 Innovation Ecosystem Network
- 58. Cultivation / Harvesting modes - value co-creation Chinese interlocks at the investment firm level Government-led investment firms Knowledge of government guarantees Investments in firms that return benefits to China Global interlocks at both investment firm and enterprise levels Opportunity network & value co-creation http://successbeginstoday.org/wordpress/wp-content/unexpected2.jpg Topline Findings
- 59. http://www.flickr.com/photos/manpsing/2618332693/ http://www.fabcats.org/owners/feeding/info.html 被动获取 主动猎取 FURTHER RESEARCH Personal relationships/opportunity networks Time series analysis Expansion of data Chinese language press releases Chinese business registries
- 60. Innovation Ecosystems Network Regional Studies with Global Perspective China, Norway, Finland
- 61. . 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.
- 62. 讨论 数据，工具，问题 www.innovation-ecosystems.org Innovation Ecosystem Network
Editor's Notes
- One of our goals is to look for patterns of innovationPattern is something that follows a ruleFor example most of the bicycles are silverOutlier is something that does not follow the ruleIn this case yellow bike could be considered an outlierBoth patterns and outliers are interestingPatterns are indicative of the current trendsOutliers are indicative of something new; that may later on become a trendAt one time both microsoft and intel could have been considered an outliersThere are very many patterns; but only some of them may be of interestFor example, parking officer may be looking for absense of permits to issue ticketsIf I am looking for my bike; i am interested in different featuresSo what makes patterns interesting or useful is partially dictated by the goalsLet me briefly touch up on the meaning of patternsThere are quite a few patterns in this pictureFor example most of bicycles tend have the same orientation---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
- Adding more dimensions may allow to identify patterns easierBut more dimmensions also required data
- Innovation happens very fast. If you are too slow – you loose.To react fast, we need the current data.
- My specialty is AI & Data MiningSo a first thing, is to get the data.There is a lot of nice data on innovation but it is not so recent. In traditional data gathering, data is often gathered over a period of time. Then it goes through various processes within organization, gets analyzed; some reports are released; and then the data is released. This process may take several years.
- So we try to get data from different source types.Social Media produces very current data, but may not always be as reliable (biased towards the public consensus)News data tends to be accurate but coverage is often limited (biased by authors views)Data from government organizations, is often of high quality, but takes years to produceWe then federate this data, and iterate between analysis and visualization
- In our project we try to understand innovationTo get a more full picture we gathered data on various aspects;Companies, people, …And also how they are interconectedWhat makes this data set different, besides its timeliness is the majority of data is about small companies having between 1 – 5 employees.A lot of innovation happens there so it is important to track; but is usually not captured.
- This shows how we have evolved from the local/regional activities
- This shows how the models of innovations have evolved reflecting the changes
- We can also look at the companies by sector
- 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
- 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
- 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
- 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.