Visual Analytic Tools for Managing Technological Innovations
Organized by Ping Wang
May 28, 2010
2
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
3
Workshop Agenda Introduction
Personal Topic
Theories of innovation Extant methods & tools Research mini-talks Discussion Conclusion
4
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
5
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
6
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
7
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 …
8
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 2003
9
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, 2010.
10
Dominant Paradigm
Fichman 2004
11
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
12
Innovation Trajectories
Time
Hype CyclePerformance S-curve
Adoption Curve
Linden & Fenn 2003
15
0
500
1000
1500
2000
2500
3000
3500
4000
1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007
Nu
mb
er o
f P
ub
licat
ion
s
0
500
1000
1500
2000
2500
3000
Inve
stm
ent/
Sal
es (
in m
illio
ns
of
$)
Web Services Publications Web Services R&D Investment Web Serivces Application Sales
Web Services Publications
Web Services R&D Investment
Web Services App Sales
Sources: LexisNexis for publications; Gartner for R&D investment; IDC for Application Sales
Web Services Trajectories
16
Event History Analysis
Worldwide Sales (in millions)
100
200
300
400
500
Source: Gartner
25%
50%
75%
100%
125%
Annual Growth
’99 ’00 ’01 ’02
Niku approaches AberdeenNiku approaches Aberdeen
’98
First report, first articleFirst report, first articlePeopleSoft PSAPeopleSoft PSA
Accenture adopts NovientAccenture adopts Novient
Gartner promotes SPOGartner promotes SPOAlex PopovAlex Popov’’s emails email
PSA 2001 conf.PSA 2001 conf.1st PSA book1st PSA book
Microsoft Microsoft ships PSAships PSA
EDS adopts EvolveEDS adopts Evolve
Wang & Swanson 2007
17
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
18
SOA
Cloud Computing
BPO
Semantic Web
Portable Personality
RFID
Tera-architectures
Business Intelligence
Mashup
Ajax
Web2.0
DRM
Ultramobile Devices
Distributed Encryption
Chatbots
Thin Provisioning
CRM
VoIP
SaaS
OSS
Application Quality Dashboards
Identity Management
SCM
We Have Lots of Innovations, But
19
… Little and Dated Understanding
19931998
20
IT Innovations in Discourse
BPR
ERP
KM
Data Warehouse
Groupware
CRM
ASP
E-Commerce
0
20
40
60
80
100
120
140
160
180
1985 1987 1989 1991 1993 1995 1997 1999 2001 2003
Adj
uste
d nu
mbe
r of
artic
les
on IT in
nova
tions
exc
ept e-
com
mer
ce
0
200
400
600
800
1000
1200
1400
1600
1800
2000
Adj
uste
d nu
mbe
r of
artic
les
on e
-com
mer
ce
BPR ERP KM Data Warehouse
Groupware CRM ASP E-Commerce
21
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
22
Gartner Magic Quadrant
27
Production of Innovations
Hage & Hollingsworth 2000
Government Investors
Universities
Vendors
Venture Capitals
Government Labs
Designers Regulators
Ad Agencies
Production of Innovations
Basic Research Applied Research Product Development Manufacturing Marketing
27
28
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 2004
29
Use of Innovations
Swanson & Ramiller 2004
User Organizations
Universities
Media
Market Researchers
Consultants
Financiers Distributors
General Public
Use of Innovations
Comprehension Adoption Implementation Assimilation Abandonment
29
30
Innovation Community
Government Investors
Universities
Vendors
Venture Capitals
Government Labs
Designers Regulators
Ad Agencies
Production of Innovations
Basic Research Applied Research Product Development Manufacturing Marketing
User Organizations
Universities
Media
Market Researchers
Consultants
Financiers Distributors
General Public
Use of Innovations
Comprehension Adoption Implementation Assimilation Abandonment
Supply
DemandCommunity for Innovation A
30Wang, Qu, & Shneiderman 2008; Wang 2009
31
Innovation Ecosystem
Government Investors
Universities
Vendors
Venture Capitals
Government Labs
Designers Regulators
Ad Agencies
Production of Innovations
Basic Research Applied Research Product Development Manufacturing Marketing
User Organizations
Universities
Media
Market Researchers
Consultants
Financiers Distributors
General Public
Use of Innovations
Comprehension Adoption Implementation Assimilation Abandonment
Supply
DemandCommunity for Innovation A
Community for Innovation B
Community for Innovation C
31Wang, Qu, & Shneiderman 2008; Wang 2009
32
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
33
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?
34
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 2010
35
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
“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
37
Popularity Driver: Social Cognition Theories
Management fashion (Abrahamson 1996) Organizing vision (Swanson & Ramiller 1997) Stickiness (Heath 2008)
Methodology Case studies Discourse analysis Content analysis
38
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?
39
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
http://terpconnect.umd.edu/~pwang/PopIT/
40
The PopIT Framework
Theoretical Framework
Diffusion of technological products and
services
Diffusion of technological
concepts
Social network
Social cognition
Empirical Research
Qualitative Case Studies
Hypotheses Differentiated status … Opinion leadership Concept relatedness …
Scalable Computational Analysis
Data acquisition
Feature selection
Modeling
Fit
http://terpconnect.umd.edu/~pwang/PopIT/
41
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
http://terpconnect.umd.edu/~pwang/STICK/
42
Innovation Knowledgebase Innovation concepts and material; persons and organizations in
the innovation community; Relationships; Descriptions
Analytic and Visualization ToolsNetwork of innovation, network of innovation
communities, trends, relationships over spatial and temporal dimensions
Multiple Data Sources
Academic Publications PopIT; Google Scholar
Trade Publications PopIT; Ebsco
Corporate Communications PopIT; Factiva
Grassroots PopIT; Wikipedia; Google Groups; Slashdot
Government Support STAR
Patents STAR
STICK
SciSIP community
Innovation Communities (Scientists; Engineers; Policy Makers Vendors; Journalists; Prospective Adopters, etc.)
1
2
3
The STICK Framework
http://terpconnect.umd.edu/~pwang/STICK/
43
BIG Picture
Social Structure
Social Cognition
Innovation Outcomes
Named EntityEvent
Relation
SentimentValues
Sensemaking
PopularityAdoption/Sales
Policy
44
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
45
Thank You from PopIT & STICK
Thanks to National Science Foundation for grants IIS-0729459 and SBE-0915645
http://terpconnect.umd.edu/~pwang/PopIT/ http://terpconnect.umd.edu/~pwang/STICK/