visual analytic tools for managing technological …terpconnect.umd.edu/~pwang/wang2010 hcil...

Visual Analytic Tools for Managing Technological Innovations

Organized by Ping Wang

[email protected]

May 28, 2010

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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

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Workshop Agenda Introduction

Personal Topic

Theories of innovation Extant methods & tools Research mini-talks Discussion Conclusion

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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

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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

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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

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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 …

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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

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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.

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Dominant Paradigm

Fichman 2004

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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

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Innovation Trajectories

Time

Hype CyclePerformance S-curve

Adoption Curve

Linden & Fenn 2003

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Gartner Hype Cycle

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Hype Cycle for Emerging IT 2009

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

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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

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Diffusion curve is not the only trajectory Other curves interact with adoption curve



innovation to the forefront Vendors and adopters are not the only actors

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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

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… Little and Dated Understanding

19931998

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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

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artic

les

on e

-com

mer

ce

BPR ERP KM Data Warehouse

Groupware CRM ASP E-Commerce

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innovation to the forefront Adopters are not the only actors

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Gartner Magic Quadrant

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Magic Quadrant: Workplace Social SW

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Forrester Wave

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Forrester Wave Demo

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Tire-Tracks: Concept $Bn Industry

Lazowska 2008

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Production of Innovations

Hage & Hollingsworth 2000

Government Investors

Universities

Vendors

Venture Capitals

Government Labs

Designers Regulators

Ad Agencies


Basic Research Applied Research Product Development Manufacturing Marketing

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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

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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

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Innovation Community


Universities

Vendors

Venture Capitals

Government Labs


Ad Agencies



User Organizations

Universities

Media

Market Researchers

Consultants


General Public

Use of Innovations


Supply

DemandCommunity for Innovation A

30Wang, Qu, & Shneiderman 2008; Wang 2009

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Innovation Ecosystem


Universities

Vendors

Venture Capitals

Government Labs


Ad Agencies



User Organizations

Universities

Media

Market Researchers

Consultants


General Public

Use of Innovations


Supply

DemandCommunity for Innovation A

Community for Innovation B

Community for Innovation C

31Wang, Qu, & Shneiderman 2008; Wang 2009

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innovation to the forefront Adopters are not the only actors

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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?

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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

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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

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Popularity Driver: Social Cognition Theories

Management fashion (Abrahamson 1996) Organizing vision (Swanson & Ramiller 1997) Stickiness (Heath 2008)

Methodology Case studies Discourse analysis Content analysis

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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?

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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/

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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/

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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/

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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/

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BIG Picture

Social Structure

Social Cognition

Innovation Outcomes

Named EntityEvent

Relation

SentimentValues

Sensemaking

PopularityAdoption/Sales

Policy

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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

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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/

[email protected]

visual analytic tools for managing technological …terpconnect.umd.edu/~pwang/wang2010 hcil...

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