1. 2013 Conference for Organisational Learning,
Knowledge and Capabilities
Washington D.C.
April 26-27, 2013
Pamela A. Posey, RamkrishnanV.Tenkasi (presenters)
and Douglas Austrom, Betty Barrett, Bert Painter, and Betsy Merck
Socio-Technical Systems Roundtable
Sociotechnical Systems inVirtual
Organizations:
The Challenge of Coordinating Work and
Knowledge AcrossTime and Space
This material is based upon work supported by the National Science Foundation
under grant number NSF OCI 09-43237. Any opinions, findings and conclusions
or recommendations expressed in this material are those of the author(s) and
do not necessarily reflect the views of the National Science Foundation.
STS inVirtual Organization 2013

2.  Virtual organizations that span institutional and
national boundaries have become central to
emerging practice of science and engineering
 Challenge: how to design effective work systems
in contexts that are:
◦ Highly Interdependent
◦ Virtual, not co-located
◦ Non-linear, involving non-routine knowledge based work
 NSF funded study of 3 on-going virtual R&D
projects across different stages of the R&D
Continuum
STS inVirtual Organization 2013

3. • Research Questions: How can we best coordinate
work and knowledge across time and space? What
are the most appropriate coordinating mechanisms
that enable effective knowledge sharing and learning
in key deliberations at different stages in the R&D
continuum?
• Key Premise: Nature of deliberations (complexity
of key choice points) and the coordinating
mechanisms required to effectively manage them
will vary based on the level of task uncertainty and
degree of equivocality at different points along the
R&D continuum.
STS inVirtual Organization 2013

4. • Selection of virtual organizations to include in study
• Scoping interviews/secondary data to place each
project on R&D continuum
• In-depth structured interviews/observations to
gather information on;
– Types of deliberations
– Knowledge barriers that impeded deliberations
– Identification of emergent and purposive coordination
mechanisms
• Detailed case study write-ups of each case
• Comparative analysis within and between cases
• “Extreme exemplar” cases (Eisenhardt and
Graebner, 2007).
STS inVirtual Organization 2013

5.  Caltech- Orchid Project: fundamental research
◦ Optical Radiation Cooling and Heating in Integrated Devices
◦ Tightly-Linked Collaboration for Design of Experiments & Device Fabrication
among Laboratories using 3 Technology platforms
◦ Pasadena, Switzerland and Austria
◦ Major challenge: creative research and design and knowledge generation in a
complex virtual setting
 NACC: a virtual R&D eco-system
◦ Comprised of 29 NIA-funded Alzheimers Disease Centers (ADCs) and the
National Alzheimers Coordinating Center Center (NACC)
◦ Major challenge: Create Uniform Data Set agreeing upon and compiling data
from the 29 different centers as the basis of research
 LVG: a large video game developer
◦ Core team with distributed vendors in Philippines, China, India, Switzerland,
North America and across the parking lot
◦ Major challenge: Cost effective game development work with high quality
and timeliness completed at a distance for art production, engineering and
testing STS inVirtual Organization 2013

6. HIGH Uncertainty LOWER Uncertainty
‘Orchid’
Project
‘Uniform
Data
Set’
Project
‘Large
Video
Game’
Project
Pure
Research
Work
DON’T
KNOW
WHAT
we
are
looking
for
DON’T
KNOW
HOW
to
carry
out
the
research
Applied
Research
Work
DON’T
KNOW
WHAT
(i.e.
end
state
or
objec?ve)
KNOW
HOW
to
carry
out
the
research
Exploratory
Development
Work
KNOW
WHAT
DON’T
KNOW
HOW
to
achieve
it
Advanced
Development
Work
KNOW
WHAT
DON’T
KNOW
HOW
IN
DETAIL
to
achieve
it
Start-­‐Up
(pilot
plants,
beta
tes?ng)
Development
Work
KNOW
WHAT
KNOW
HOW
CONCEPTUALLY
to
achieve
it
Scale-­‐Up
(volume
&
costs)
Development
Work
KNOW
WHAT
KNOW
HOW
OPERATIONALLY
to
achieve
it
R
1
R
2
D
1
D
4
D
2
D
3
STS inVirtual Organization 2013

7. • Orchid
– What experiment shall we run?
– How shall we design the experiment?
– How shall we execute the experiment?
– How do we make sense of the results?
• NACC
– What data will go in the UDS?
– What diagnostic instruments shall we use?
– Who will have access to the data?
• LVG
– What new features shall we develop?
– What contractor shall we use for this work?
– What will the requirements be for the contractor?
STS inVirtual Organization 2013

8. Lack of knowledge
Example: Need to invent a new methodology so devices created at
Caltech could run on different experimental equipment in Europe
(ORCHID)
Failure to use knowledge
Example: failure to use knowledge that other divisions had about
vendors capabilities (LVG, Inc.)
Failure to share knowledge
Example: desire to collect data related to a particular research interest
did not address need for shared standardized data (NACC)
Lack of a common frame of reference
Example: related but quite different disciplinary roots in ORCHID
project

9.  Standards - rule based
◦ Example: data formats, process standardization)
 Plans – result based
◦ Example: delivery schedules, project milestones
 Formal mutual adjustment
◦ Example: hierarchy, task force
 Informal mutual adjustment
◦ Example: informal meetings, temporary co-location
SocialSystemTechnicalsystem

10. STS inVirtual Organization 2013

11.  All categories of coordinating mechanisms were used to
some degree by all 3 sites
 Technical (structural) coordinating mechanisms were used
more in activities and projects with lowest uncertainty and
equivocality
 Social (mutual adjustment) coordinating mechanisms were
used more in activities and projects with higher uncertainty
and equivocality about process and outcomes
 Identifying the location of a virtual work project on the R&D
continuum helps us anticipate the nature and degree of the
coordination challenge and the mechanisms most likely to
help

12. • Knowledge barriers increase as task uncertainty
increases
• Appropriate coordinating mechanisms can help
overcome knowledge barriers that hamper
deliberations
• Coordinating mechanisms should match the
information processing complexity demanded
by task uncertainty of the deliberation