STICK: Science & Technology Innovation Concept Knowledge-base - PowerPoint PPT Presentation

AAAS+NSF SciSIP Workshop: Building a Community of Practice II Washington, DC, October 19, 2010 STICK: Science & Technology Innovation Concept Knowledge-base Ping Wang Ben Shneiderman Yan Qu

Why Do We Develop STICK?  Artificial or disciplinary divide between  Science and technology  Invention and innovation  Innovation production/supply and use/demand  Over-emphasis on success  Lack of tools for analyzing large-scale data So…  We develop STICK to capture large-scale, multi- source, multi-field, longitudinal data on successful and failed innovations and to advance visual analytic tools for using such data. 2

Where Do Data Come From?  Patents  News & trade press  ProQuest newspapers  USPTO Full-text & image database (PatFT)  Lexis-Nexis and application full-  Factiva text & image database  Scholarly work (AppFT)  Web of science  Google patents  Google scholar  Government funding  ProQuest dissertations  NSF and NIH awards  ACM Digital Library  Social/informal media  Company data  Wikipedia  Hoovers  Techmeme  Wharton Research Data  Slashdot Services (WRDS) 3

What Is In STICK?  Three fields  Relationships  IT, biotech, & nanotech  Among innovations  Broader, narrower,  Innovations complementary,  Concepts competing …  Products/services  Among actors  People/organizations  Collaborative, affiliated transactional,  Developers/producers competitive …  Universities, research  Btw innovation & actor labs, vendors  Invent, commercialize,  Adopters/users invest, adopt …  Companies, nonprofits  Time of relationship  Intermediaries 4

Is STICK Useful? (Study 1) Trade Press TM=Treemaps CT=Cone Trees HT=Hyperbolic Trees Articles Academic Papers Trajectories help tell Innovation success/failure stories. Patents 5 Shneiderman et al. 2010

Popularity shapes impact Is STICK Useful? (Study 2) of innovation. 180 2000 Adjusted number of articles on IT innovations except e-commerce ERP E-Commerce 1800 160 CRM 1600 140 Data Warehouse Adjusted number of articles on e-commerce 1400 120 KM 1200 100 BPR 1000 80 800 60 600 ASP 40 400 Groupware 20 200 0 0 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 BPR ERP KM Data Warehouse 6 Groupware CRM ASP E-Commerce Wang 2010

Dashboard helps Is STICK Useful? (Study 3) monitor relations among innovations. DLearn DLearn DLearn Telecommute Telecommute Telecommute DSL DSL DSL SmartCard SmartCard SmartCard DigiCam DigiCam DigiCam Bluetooth Bluetooth Bluetooth MP3 MP3 MP3 VPN VPN VPN PDA PDA PDA TabletPC TabletPC TabletPC Multimedia Multimedia Multimedia GPS GPS GPS Grpware Grpware Grpware ASP ASP ASP WiFi WiFi WiFi EDI EDI EDI eCom eCom eCom eBiz eBiz eBiz IM IM IM SFA SFA SFA SCM SCM SCM iPod iPod iPod Outsource Outsource Outsource KM KM KM ERP ERP ERP iPhone iPhone iPhone DW DW DW Linux Linux Linux CRM CRM CRM WebServ WebServ WebServ OLAP OLAP OLAP Blog Blog Blog AI AI AI BI BI BI RFID RFID RFID NeuralNet NeuralNet NeuralNet SocNet SocNet SocNet MySpace MySpace MySpace ExpertSys ExpertSys ExpertSys YouTube YouTube YouTube DecisionSS DecisionSS DecisionSS Web2.0 Web2.0 Web2.0 OSS OSS OSS Wiki Wiki Wiki SOA SOA SOA Wikipedia Wikipedia Wikipedia Virtualization Virtualization Virtualization BizProReen BizProReen BizProReen UtiComp UtiComp UtiComp 7 Tsui et al. 2010 CloudCom CloudCom CloudCom

Dashboard helps Is STICK Useful? (Study 4) monitor innovation, community, and their interactions. 8 Zhang et al. 2011

How to Make STICK More Useful?  STICK helps researchers & policy makers  Monitor and make sense of innovations  Find evidence in theorizing and policy-making  Social computing system allows users to  Specify fields and domains  Suggest innovations to study  Specify level of abstraction  Suggest additional attributes  e.g., innovation benefits, unintended consequences … 9

Who Are We? PopIT: Scalable Computational Analysis STICK: Science & Technology Innovation of the Diffusion of Technological Concepts Concept Knowledge-base Thanks to National Science Foundation for grants IIS-0729459 (HSD) and SBE-0915645 (SciSIP) 10 STICK.ischool.umd.edu pwang@umd.edu

References  Shneiderman, B., Wang, P., Qu, Y. and Dunne, C. Analyzing Trends in Science & Technology Innovation, in Proceedings of the 27th Annual Human-Computer Interaction Lab Symposium , College Park, MD, 2010.  Tsui, C.-j., Wang, P., Fleischmann, K.R., Oard, D.W., and Sayeed, A.B., Exploring the Relationships among ICTs, in Proceedings of the 43rd Hawai'i International Conference on System Sciences , Kauai, HI, 2010.  Wang, P., 2010, Chasing the Hottest IT: Effects of Information Technology Fashion on Organizations, MIS Quarterly , 34(1), pp. 63-85.  Zhang, P., Qu, Y., and Huang, C., Designing a Multi-layered Ontology for the Science and Technology Innovation Concept Knowledge-base, in Proceedings of the 44th Hawai'i International Conference on System Sciences , Kauai, HI, 2011. 11