presentation: sociotechnical systems in virtual organizations: the challenge of coordinating work...
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2013 Conference for Organisational Learning, Knowledge and Capabilities
Washington D.C. April 26-27, 2013
Pamela A. Posey, Ramkrishnan V. Tenkasi (presenters) and Douglas Austrom, Betty Barrett, Bert Painter, and Betsy Merck
Socio-Technical Systems Roundtable
Sociotechnical Systems in Virtual Organizations:
The Challenge of Coordinating Work and Knowledge Across Time 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 in Virtual Organization 2013
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 in Virtual Organization 2013
• 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 in Virtual Organization 2013
• 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 in Virtual Organization 2013
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 in Virtual Organization 2013
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 in Virtual Organization 2013
• 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 in Virtual Organization 2013
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
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
Soci
al S
yste
m
Tec
hnic
al s
yste
m
STS in Virtual Organization 2013
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
• 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