Download collaboration technology, experimental evaluation

Ann Majchrzak
Professor of Information Systems
Marshall School of Business
USC
Background
• PhD from UCLA (1980) in Social Psychology,
minors in Computer Science, Statistics, &
Research/Evaluation Methods
• Westat Contract researcher on Organizational
Effectiveness for Marine Corps & Army
• Professor of Organizational Behavior, Purdue
• Professor of Human Factors, USC
• Professor of Information Systems, USC
(teach Business Process reengineering, UML, systems
analysis, use case, agile & iterative SW development
methods, brokering outsourcers)
Research Area
• How to facilitate cross-disciplinary innovative problemsolving with technology: Creation of technology design
principles and business process practices
Example projects:
- @ Aerospace: Knowl Mgt technologies
- @ Local Defense Contractor: Features of Collaboration
Technologies affecting innovation
- @ Rocketdyne: case study of agile innovative engineering done
virtually
- @ JPL: case studies of knowledge reuse for innovation
- NSF-sponsored study on how to foster rapid innovation in agile
emergent teams
- SIM-sponsored studies on use of wikis & mashups to foster
innovative problem-solving
@ Aerospace
Tenure
Ease of Use of
Information
Systems
-0.181*
(2.33)
0.154
(1.85)
In-person Interactions
0.489**
(7.31)
Path coefficients with t-values in parentheses
*Significant at .05 level
**Significant at .01 level
Shaded circles represent control variables
Use of ISs for
Cross-domain
Knowledge
Transformation*
Perceived
Openness of
Communication
0.091
(.93)
0.367**
(3.56)
Value of Other
Domain Knowledge
(R2 = 0.302)
0.133
(1.49)
.114
(1.11)
0.110
(.97)
Perceived Task
Complexity
Number of IS
Used
Dashed arrows represent insignificant relationships
PhD Dissertation: I. Faniel:
Influencing Individual Innovation
Through Technology Features
that Support Cross-departmental
Understanding
*Identify others that may have knowledge needed to solve the problem
Make associations among different instances of knowledge to broaden view on
how the problem can be defined
Identify existing assumptions about and constraints on the knowledge that may
important to the problem at hand
Compare and contrast various approaches to solving the problem
Find knowledge at different levels of specificity
Identify how constraints, assumptions, points of view about knowledge related
to a past problem definition or solution have changed
TECHNOLOGY SUPPORT FOR VIRTUAL COLLABORATION
FOR INNOVATION IN SYNCHRONOUS AND
ASYNCHRONOUS INTERACTION MODES
Input
Process
Outcomes
Synchronous Interaction Mode
CT Support for
Testing and
Adjusting Strategy
Sharing Diverse
Knowledge
Learning
CT Support for
Contextualization
Strategy
Reflection on Others’
Shared Knowledge
Asynchronous Interaction Mode
CT Support for
Attention Focusing
Strategy
CT Support for
Contextualization
Strategy
CT Support for
Perspective Taking
Strategy
Sharing Diverse
Knowledge
Reflection on Others’
Shared Knowledge
Innovation
VC3 Teams:
Virtual Creative Collaborations
Crossing organizations: Case of SLICE
• Produced Prototype made of 6 parts instead of
normal 1200 (200 fold decrease)
• Predicted quality level of >6 sigma
• First unit cost - $47K instead of $4.5M
• Est. engine mfg. cost - $0.5M instead of $7M (14fold decrease)
• Normal cycle time - 10 months vs of 6 years
It’s not just the Technology
PRECURSOR TO INTRODUCTION
CREATING SHARED
UNDERSTANDING:
DEFINING TECHNOLOGY
USE PROTOCOLS
CREATING TRUST:
ESTABLISHING
UMBRELLA
AGREEMENTS
CHANGES
FROM IN-USE
ADAPTATIONS
FLEXIBLE ADOPTION
OF
TECHOLOGY
FUNCTIONALITY
ADAPTATION
PROCESS
ADAPTATION or CREATION
of WORK
PROCESSES
SUCCESSFUL OUTCOMES
FINAL PROTOCOL
* = change from initial
KNOWLEDGE CAPTURE
Formal review
minutes only
By others
KNOWLEDGE SHARING
AS-IS TEAM
Need-to
-know basis
Minimal (drawings
& minutes)
Capture only when
told
Team
uses a
common
tool
Everything
in
Notebook
Use others
MinimalCapture only
when told
Restrict
All in Notebook
access to
shared
team only
PROTOCOL
Do own
Use searches and
Synchronous Synchroothers
nous only
add links
Make
to find
Facilitate assumptions
participation
explicit
Participative
Constraints
challenged
Constraints
challenged
Experts work directly
with management
AS-IS MANAGEMENT
DECISION MAKING
Use no tool
Synchronous
only
Model of Knowledge Reuse
For Innovation at JPL
Use of weak & strong ties
Variety of search methods
Intentionally
define
problem to be
innovative
(DP)
Conduct broad
search for multiple
reusable ideas (S)
Breadth with which indivs
define search space
Insurmountable perf gap
Decision to
not invent
Developing
and evaluating
conceptual
approach
which is
ambitious, not
tied to past,
and postpones
detailed
consideration
of constraints.
(CA)
Briefly evaluate alternative
ideas for implementing
conceptual approach wrt
credibility, relevance &
adaptability (BE)
Finalize selected
idea into
solution (F)
In-depth analysis
of adaptations
required of each
idea & how to
make them (IA)
Knowing
existence of
meta-info
Being able
to act on
meta-info
My research approach
• Systematic analysis of pilot interventions
• Create “socio-technical” metrics to identify
predictive and in-process factors affecting
innovation
• Stakeholder observations
• Conversational/textual analysis
• Identify key processes & technology features
that together produce the result
Figure 1: Ratings of SLICE members at Project End on
Usefulness of Notebook Features for Information Retrieval
[1 = not useful at all; 5 = very useful]
Example metrics
Hot Links
Reference Links
Remote Access
Snapshots
Authoring Entries
e-mail Notification
Navigation
Sketching
Template
1
2
3
4
5
Work process
Effort
Required
Technology
Strategy
Time
1.1 [DP] R reads AO. Problem
is: “in 45 days develop a tiny
lightweight instrument that
will autonomously detect and
measure dust devils on Mars
to characterize strength and
frequency of hazard to later
human exploration”
Example Method
1.2[CA] R considers tradtl
soln: measuring inside
weather phenomenon using
std meteorological solns.
Decides soln isn’t sufficient
since doesn’t provide adv’d
warning, 3d imaging, or
measures of velocity, size &
accompanying phenomenon
1.10 Project Engr’s husband
serendipitously installs on his
computer initial results of a JPL
program to develop a laser
range finder which provides info
about where rocks and hazards
are in order to autonomously
guide a lander during landing
1.13 [BE] R asks himself:
maybe we can convert a
scanning laser range finder
from scanning for rocks to
scanning for dust devils (Dec
to adapt Alt#3)
1.11 Project engr
suggests to R to look at
husband’s data to see the
kind of data one gets
from lasar range finders
1.14 [BE] R asks other team
members to see husband’s data
to evaluate Alt#3
1.15 [CA] R meets with team
expert on Lidar to determine
costs & risks of Lidar approach
1.20 [IA] R examines data from Alt#3b firm’s
prototype and meets with firm about Alt#3b. In
house ballpark costing indicates possibility of cost
overrun if Idea #3a developed (Alt#3a discarded)
1.25 [IA] R examines Alt#3b
firm’s proposals for
prototype for Alt #3b &
determines its too big/heavy
for size/mass of instrument
(Dec to adapt Alt#3b)
1.26 [IA] R works with
team members to
come up with ideas to
make Alt#3b smaller
by integrating with a
camera from U of Ariz
R=reuser; AO=Announcement of Opportunity
1.3 [DP] R defines
probl to
require innovation:
provide info on
dust storms so
humans will have
enough info
to understand &
predict hazardous
weather conditions
1.4[CA] R uses Radar as an
analogy for the operating
principle: wants a system to do
for dust devils what radars do
for thunderstorms. Since radar
can’t measure dust, substitutes
Lidar since knows dust affects
light and Lidar measures light
1.7 [S] R contacts
engrs, scientists, rover
operations, researchers
via internet, friends
1.5 [BE] R asks: can we do it
ourselves? (Alt#2 discarded
as too expensive
1.9 [BE] Russian space
program collapsed & unavailable.(Alt#1discarded).
1.12 [CA] R looks at husband’s data
which visually indicates benefits of
concept of using laser range finder
1.16 [S] R goes to see
“old buddy” involved
in Lasar Range Finder
project to learn more
about it
1.21 [IA] Team expert conducts
analytic studies of Alt#3b
concluding meets “borderline”
mass, volume, & power reqs
1.6 [S] R defines search
to include finding lidars
with diff functions (sky,
hazards terrain) in diff
conditions (stationary,
scanning)
1.17 [BE] R discovers 2
prototypes for rock scanning
had already been built by 2
firms identified by buddy as
reputable: one in US (Alt#3a)
and one in Canada (Alt#3b)
1.22 [S] Team member
examines AO to get names to
contact to get CSA names.
Contacts CSA and asks them
to donate Lidar for Alt#3b.
1.27 [IA] Team expert has several subsequent
meetings with Alt#3b firm to determine if
adaptations to Alt#3b can be made
1.28 [BE] U of Ariz scientists suggest
Alt #4. Team looks at Alt#4 but since
Alt#3b is “free”, Alt #4 considered
fallback
1.8 R remembers
Russian experiment
on Mars 98 using
Lidar (Alt#1)
1.18 [BE] R remembers that
AO says Canadian Space
Agency (CSA) willing to donate
to mission. Has idea CSA
might help with cost
1.19 [BE] R contacts Canadian
firm to see if interested
1.23 [BE] CSA agrees if
request comes from
Can scientists.
1.24[S] R
searches
internet and
finds Canadian
scientists
1.29[F] Team expert & Alt#3b firm work to
make adaptations to Alt#3b
1.30 [IA] Team expert &
Alt#3b firm builds separate
software models to improve
performance of Lidar.
1.31 [F] Data exchanged by
emails/phone to converge on
final solution
Example Analysis
DISCONFIRMING
EVENTS
Sources of Structure:
TECHNOLOGY
GROUP
ORGANIZATION ENV’T
APPROPRIATION
MOVES and
FAITHFULNESS
DECISION
PROCESS
POSITIVE
OUTCOMES
MISALIGNMENT
ALIGNMENT
PREEXISTING
STRUCTURES
EMERGENT
STRUCTURES
Questions?