Visual Analytic Tools for Managing Technological Innovations

Transcription

1 Visual Analytic Tools for Managing Technological Innovations Organized by Ping Wang May 28, 2010 Our Goals Today People sharing common interests meet up Welcome external attendees PopIT and STICK joint meeting Welcome new team members Draw the big picture To see how different pieces fit together To foster convergence and integration Conclude the past year Plan for the summer and new year 2

2 Workshop Agenda Introduction Personal Topic Theories of innovation Extant methods & tools Research mini-talks Discussion Conclusion 3 Personal Introduction Your name, group/dept, & organization Your research/professional interests An interesting fact about you beyond work Ping Wang, Maryland s ischool, CIPEG, HCIL, and R.H. Smith School of Business Drivers & impacts of popular technologies Tennis fanatic 4

3 Managing Innovations This really is an innovative approach, but I m afraid we can t consider it. It s never been done before. Cartoon by Aaron Bacall 5 Innovation An innovation is an idea, practice, or object that is perceived as new by an individual or other unit of adoption. Innovation, invention, and change 6

4 Types of Innovations Individual vs. organizational: ipod vs. CRM Incremental vs. radical: Windows XP, vista, 7... External vs. internal: Facebook vs. Yammer Subject areas Technological: IT, biotech, nanotech... Structural: Matrix organization Strategic: Search engine to Google Cultural: The green movement Product/service: iphone/iphone Apps Process: Business Process Reengineering 7 Diffusion of Innovation The process by which an innovation spreads over time among individuals or organizations Involves communication leading to adoption Typically involves more imitation than it does invention An adopter innovates relatively early or late compared to others Rogers

5 Applause, Please, for Early Adopters Buying on Day 1: Sayuri Watanabe came from Japan to be among the first to get an ipad last month at the Apple store in downtown San Francisco. New York Times, May 7, Dominant Paradigm Fichman

6 New Dimensions Other curves Adoption curve is not the only trajectory Other curves interact with adoption curve Other innovations Enough single-innovation studies Innovations interrelated in interesting ways Other actors Bring context/environment/background of innovation to the forefront Vendors and adopters are not the only actors 11 Innovation Trajectories Hype Cycle Performance S-curve Adoption Curve Time Linden & Fenn

7 Gartner Hype Cycle 13 Hype Cycle for Emerging IT

8 Web Services Trajectories Web Services App Sales Number of Publications Web Services Publications Investment/Sales (in millions of $) Web Services R&D Investment Web Services Publications Web Services R&D Investment Web Serivces Application Sales 0 Sources: LexisNexis for publications; Gartner for R&D investment; IDC for Application Sales 15 Event History Analysis Niku approaches Aberdeen First report, first article PeopleSoft PSA Accenture adopts Novient EDS adopts Evolve Gartner promotes SPO Alex Popov s s PSA 2001 conf. 1st PSA book Microsoft ships PSA 125% 100% 75% 50% 25% Source: Gartner Worldwide Sales (in millions) Annual Growth 16 Wang & Swanson 2007

9 New Dimensions Other curves Diffusion curve is not the only trajectory Other curves interact with adoption curve Other innovations Enough single-innovation studies Innovations interrelated in interesting ways Other actors Bring context/environment/background of innovation to the forefront Vendors and adopters are not the only actors 17 We Have Lots of Innovations, But Portable Personality Mashup OSS SOA Semantic Web Web2.0 Tera-architectures Ajax Ultramobile Devices Identity Management SCM SaaS BPO Chatbots Thin Provisioning DRM RFID Business Intelligence Cloud Computing CRM Application Quality Dashboards VoIP Distributed Encryption 18

10 Little and Dated Understanding IT Innovations in Discourse Adjusted number of articles on IT innovations except e-commerce Groupware BPR Data Warehouse ERP ASP E-Commerce CRM KM Adjusted number of articles on e-commerce BPR ERP KM Data Warehouse Groupware CRM ASP E-Commerce 20

11 New Dimensions Other curves Diffusion curve is not the only trajectory Other curves interact with adoption curve Other innovations Enough single-innovation studies Innovations interrelated in interesting ways Other actors Bring context/environment/background of innovation to the forefront Adopters are not the only actors 21 Gartner Magic Quadrant 22

12 Magic Quadrant: Workplace Social SW 23 Forrester Wave 24

13 Forrester Wave Demo 25 Tire-Tracks: Concept $Bn Industry Lazowska

14 Production of Innovations Government Investors Venture Capitals Vendors Designers Production of Innovations Basic Research Applied Research Product Development Manufacturing Marketing Universities Government Labs Ad Agencies Regulators Hage & Hollingsworth Use of Innovations Comprehension- understanding the innovation in terms of its concept, principle, and purpose. Adoption- deciding whether and when to undertake the innovation, making a resource commitment Implementation- undertaking the project, making it happen, bringing the innovation to life for its users Assimilation- making the innovation a part of routine, everyday practice Swanson & Ramiller

15 Use of Innovations User Organizations Market Researchers Media Financiers Use of Innovations Comprehension Adoption Implementation Assimilation Abandonment General Public Distributors Universities Consultants Swanson & Ramiller Innovation Community Community for Innovation A Demand Government Investors Venture Capitals User Organizations Market Researchers Vendors Designers Production of Innovations Basic Research Applied Research Product Development Manufacturing Marketing Universities Government Labs Ad Agencies Regulators Media Financiers Use of Innovations Comprehension Adoption Implementation Assimilation Abandonment Universities Consultants General Public Distributors Supply Wang, Qu, & Shneiderman 2008; Wang

16 Innovation Ecosystem Community for Innovation C Community for Innovation A Demand Government Investors Venture Capitals User Organizations Market Researchers Community for Innovation B Vendors Designers Production of Innovations Basic Research Applied Research Product Development Manufacturing Marketing Universities Government Labs Ad Agencies Regulators Media Financiers Supply Use of Innovations Comprehension Adoption Implementation Assimilation Abandonment Universities Consultants General Public Distributors Wang, Qu, & Shneiderman 2008; Wang New Dimensions Other curves Diffusion curve is not the only trajectory Other curves interact with adoption curve Other innovations Enough single-innovation studies Innovations interrelated in interesting ways Other actors Bring context/environment/background of innovation to the forefront Adopters are not the only actors 32

17 Research Program on Popularity Popularity: Innovation s state of being frequently encountered and quality of being commonly favored in social context Impacts: What impacts do popular innovations have on people and organizations? Drivers: Why do some innovations come to be highly popular, while others don t? 33 Impacts: Popularity Matters Performance Reputation CEO pay Association (t-1) n.s. + + Association (t-2) n.s. n.s. n.s. Association (t-3) n.s. n.s. n.s. Investment (t-1) Investment (t-2) n.s. n.s. n.s. Investment (t-3) + n.s. n.s. Wang 2010a&b, ComputerWorld

18 Popularity Driver: Social Structure Theories Strength of weak ties (Granovetter 1973) Structural holes (Burt 1995) Opinion leader (Valente & Davis 1999) Scale free network (Barabási 2002) Methodology Social network analysis 35 When the diffusion process is socially meaningless, as in the spread of measles, physical contact may be all that is required for transmission to occur. When adoptions are socially meaningful acts, it is common to think of actors as making different choices cognitively available to each other, developing shared understandings, and exploring the consequences of innovation through each other's experience. David Strang & John Meyer 1994

19 Popularity Driver: Social Cognition Theories Management fashion (Abrahamson 1996) Organizing vision (Swanson & Ramiller 1997) Stickiness (Heath 2008) Methodology Case studies Discourse analysis Content analysis 37 New Dimensions, New Challenges... Mapping/tracking innovations Messy, noisy, and tremendous data State-of-the-art visualization tools have not been applied to this task often Understanding innovations Multiple measures of success Multiple explanations/theories at different levels Data too messy for empirical studies Shaping innovations Can we predict? How? Can we intervene? How? 38

20 The PopIT Project Scalable Computational Analysis of the Diffusion of Information Technology Concepts To understand the dynamic social system and processes underlying the development, diffusion, and use of IT innovations Sentiment and IT innovations Values and IT innovations To integrate computational analysis of text with theory building and testing in social science research 39 The PopIT Framework Theoretical Framework Empirical Research Diffusion of technological products and services Diffusion of technological concepts Social network Qualitative Case Studies Hypotheses Differentiated status Opinion leadership Scalable Computational Analysis Data acquisition Feature selection Social cognition Concept relatedness Modeling Fit 40

21 The STICK Project Science & Technology Innovation Concept Knowledge-base Long-term goals Build a large-scale, multi-source, longitudinal database of IT, biotech and nanotech innovations Develop visual analytic tools for monitoring and sensemaking Theorize differentiated innovation trajectories Near-term goals Design ontology for relationships among innovations & people Case studies of innovation trajectories Cloud computing Tree visualizations: treemaps, cone trees & hyperbolic trees 41 The STICK Framework SciSIP community 3 Innovation Communities (Scientists; Engineers; Policy Makers Vendors; Journalists; Prospective Adopters, etc.) STICK 2 Analytic and Visualization Tools Network of innovation, network of innovation communities, trends, relationships over spatial and temporal dimensions 1 Innovation Knowledgebase Innovation concepts and material; persons and organizations in the innovation community; Relationships; Descriptions Academic Publications PopIT; Google Scholar Trade Publications PopIT; Ebsco Multiple Data Sources Corporate Communications PopIT; Factiva Grassroots PopIT; Wikipedia; Google Groups; Slashdot Government Support STAR Patents STAR 42

22 BIG Picture Social Structure Named Entity Event Relation Social Cognition Innovation Outcomes Popularity Adoption/Sales Policy Sentiment Values Sensemaking 43 Research Mini-Talks Data collection & processing Chen Huang and Jia Sun Sentiment mining Amy Weinberg Human values content analysis Ken Fleischmann and An-Shou Cheng Automatic taxonomy development Chia-jung Tsui Ontology design Pengyi Zhang Social computing mechanisms Yan Qu 44

23 Thank You from PopIT & STICK Thanks to National Science Foundation for grants IIS and SBE