1. 1
"SOCIOTECHNICAL SYSTEMS IN VIRTUAL ORGANIZATION:
THE CHALLENGE OF COORDINATING WORK AND
KNOWLEDGE ACROSS TIME AND SPACE"1,2
Key Words: Sociotechnical, Virtual, Deliberations, Knowledge Coordination
Authors3
:
Pamela A. Posey, D.B.A. - pam@eyesonperformance.com; Sociotechnical Systems
Roundtable
Bert Painter - BertPainter@moderntimesworkplace.com; Royal Roads University
Douglas R. Austrom, Ph.D. - daustrom@tpaconsulting.com; Sociotechnical Systems
Roundtable
Betty Barrett, Ph.D. - bbarrett@mit.edu; Massachusetts Institute of Technology
Betsy Merck - betsy@merckconsulting.com; Sociotechnical Systems Roundtable
Ramkrishnan V. Tenkasi, Ph.D. - rtenkasi@ben.edu; Benedictine University
ABSTRACT
This study of virtual R&D projects uses Sociotechnical Systems analysis to understand the
influence of virtuality on deliberations and knowledge development at various stages of an
R&D continuum ranging from basic research to commercial development. It adds to our
understanding of how collaborative virtual work is coordinated and learning is
disseminated across time and space. Different types of coordinating mechanisms and their
effect on managing knowledge development barriers across the R&D continuum are
identified, and findings suggest that understanding the position of a project on the
continuum can help organizations select the most appropriate coordinating mechanisms to
enhance their effectiveness in a virtual work environment.
1
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 in this material are those of the author(s) and do not necessarily reflect
the views of the National Science Foundation.
2
This paper was developed in part from a working paper drafted by Bert Painter (a co-
author and member of this research team) in September 2012 entitled “Coordination in
Virtual Organization of Research and Development”.
3
Corresponding author: Pamela A. Posey, D.B.A. 4835 W Glenhaven Drive, Everett, WA
98203; Voice 425.985.6097; FAX 425.297.9087; Email pam@eyesonperformance.com

2. 2
1. INTRODUCTION
Research and development (R&D) in organizations is characterized by high interaction
needs and interdependencies among organizational units as well as with suppliers,
customers, and competitors in today’s complex global environment. Cross-industry, cross-
discipline, network-based ecosystems are emerging as a result of these increasing
interdependencies. Virtual organizations that span institutional and national boundaries
have become central to the emerging practice of science and engineering (Industrial
Research Institute, 2011; Nobelius, 2004; Gassmann et al., 2003). As such systems
proliferate, both between and within organizations, it becomes vitally important to better
understand how to design work systems that are highly interdependent yet not co-located,
how to lead and manage the innovation process, and how to coordinate the work across
time and space, as well as across multiple organizations or organizational units.
This comparative study of ongoing R&D projects conducted in virtual, geographically
dispersed organizations reinforces the importance of understanding and managing the
challenge of coordinating work and knowledge across time and space. The organizations
and projects studied represent different stages in an R&D process continuum that range
from basic research to scale-up and commercial development. Using Sociotechnical
Systems (STS) analysis as a methodological approach, the research has focused on
understanding the influence of virtuality on deliberations and knowledge development at
various stages of the R&D continuum. STS offers both theoretical and practical lenses
through which to examine the social and technical subsystems of work, whether co-located
or virtual, to determine how the design of the work and quality of fit with its goals
influence the effectiveness of the outcomes. The research project sought to add to our
understanding of how collaborative work is coordinated and how learning is disseminated
in virtual work systems. Building on the theory of organizations as information processing
and knowledge utilization systems, the research has identified different types of
coordinating mechanisms used to manage and support virtual work and has investigated
their effect on managing knowledge development barriers across the R&D spectrum.
Findings suggest that understanding the position of an organization or project on the R&D
continuum can help organizations determine and apply the most appropriate coordinating
mechanisms to enhance effectiveness in a virtual work environment.
2. RESEARCH SITES AND METHODOLOGICAL APPROACH
Three ongoing virtual R&D projects have been included in this study; each project is in a
different industry and each deals with different challenges based on the type of virtual
work being done. R&D has been characterized as an intrinsic learning system (Purser et
al., 1992) with multiple stages. Each stage is defined by the degree to which participants
do or do not know the “what” (objective) or the “how” (method or means) of their
knowledge development and synthesizing activities. These stages form a developmental
continuum that ranges from projects with high uncertainty in which participants don’t
know what is the final or real objective in concrete terms and don’t know how to
operationalize it – to projects with low uncertainty in which participants know what they
need to achieve and also know how to achieve it operationally.

3. 3
In addition to being clearly identified as R&D projects, each of the projects in this study
has been conducted in its own virtual organizational setting. In each case, the work is
comprised of interdependent knowledge-based tasks conducted by participants who are
dispersed across space and time and are unable to collaborate face-to-face all or most of
the time. Thus, each exemplifies the primary characteristics of virtual organizations as
identified in ongoing research into the nature of virtuality (Dixon and Panteli, 2010;
Schweitzer and Duxbury, 2009; Gibson and Gibbs, 2006; Chudoba et al., 2005; Malhotra
and Majchrzak, 2004). In addition, each project included an element of multidisciplinarity
or cross–professional membership.
Each project studied is located at a different stage on the continuum of the R&D process,
and each displays a different level of uncertainty in the project work. The “Orchid Project”
was a pure research project (R1 on the R&D continuum); The “Uniform Data Set Project”
was initially studied in the early development stage (D1 on the R&D continuum); and the
“Large Video Game (LVG) Project” was primarily positioned in the scale-up stage (D4 on
the R&D continuum) although the engineering aspects of this project more closely aligned
with the start-up stage (D3) of development (see Figure 1).
Figure 1: A Six-Stage Continuum of the R&D Process, including Location of Case Study
Projects on the R&D Continuum 4
4
Carolyn Ordowich (personal communication, March 26, 2009) adapted the R&D
continuum from a research portfolio model originally developed and deployed at Bell
Laboratories (Mashey, 2008; Revkin, 2008).

4. 4
The degree of uncertainty within any given project is important to recognize because of the
different methods organizations develop and implement to coordinate their activities at
various stages. Coordination is a particularly important challenge in virtual organizations.
The three cases in this study each have distinctive mechanisms that emerged or were
designed to meet the challenges faced by the members of the organization as they worked
in virtual environments. While some coordinating mechanisms were apparent across the
continuum, different coordinating mechanisms also appeared at each different stage of the
continuum. These distinctive coordinating mechanisms and the character of the structures
devised to facilitate organizational function appear to be influenced by the level of
uncertainty faced in the project. Hence, the relationship between the type of coordinating
mechanism, the degree of uncertainty in the project, and the position on the R&D
continuum are tightly coupled.
2.1 The Research Sites
The “Orchid” Project represents the field of fundamental, basic research and appears at
position R1 on the R&D continuum; it is a collaborative project among theoretical and
experimental physicists from research universities around the world. The project, funded
by the Defense Advanced Research Projects Agency (DARPA), is lead by scientists from
Caltech and includes physicists from universities in the U.S., Austria, and Germany. It is a
pure research study in which the researchers don’t know what they are going to find and
therefore, don’t know how to design a research project that will actually be effective. The
degree of virtuality is quite high in the patterns of interaction between faculty and students
or post docs.
The “Uniform Data Set” Project (UDS) is a joint project among 29 Alzheimer's Disease
Centers across the United States and the National Institute of Health. The project was
initially located at position D1 on the R&D continuum – the parties know what their goal
is but don’t know how to accomplish it. This is a mature project that is expanding its
investigation based on earlier accomplishments. The chief participants have worked
together for a number of years under the overall guidance of the National Alzheimer’s
Coordinating Center. In addition, there are substantial professional ties within and across
the centers as the membership consists of a majority of the world’s experts in Alzheimer’s
Disease treatment. This project has a number of long standing practices for
communicating both virtually and face-to-face.
The “Large Video Game” Project (LVG) involved some Start-Up Development (D3) and
mostly Scale-Up Development (D4) activities; it incorporates art asset production,
engineering, and testing activities shared among the game developer and vendors around
the world. Clarity of purpose and outcome is crucial in the D4 positioning of LVG, and
though uncertainty about the what and, to a somewhat lesser extent, the how of the process
is low, there is a high degree of virtuality and relatively low face-to-face collaboration in
this project.
All three projects exhibited high virtuality, multidisciplinary activity, and varying levels of
complexity and uncertainty in the work to be done and the processes required to complete
the work. There was clear variation in the degree of knowledge of what were the goals of
each project, how the work was to be accomplished, and how virtuality was to be
managed. Over time a variety of coordinating mechanisms emerged, were used, or were
designed to deal with the barriers to communication and knowledge development that
arose.

5. 5
2.2 Theoretical Background and Methodological Approach
In virtual organizations that involve activities such as R&D and/or new product
development, work is non-linear and knowledge based. This means that much of the work
itself is conducted through discussions, decision-making, and choice-making interactions
that are not face-to-face; these are referred to as deliberations in sociotechnical systems
theory. Deliberations are defined as “patterns of exchange and communication with
ourselves or others to reduce the equivocality of a problematic issue” (Pava, 1983, p.58),
where problematic issues are significant enough that they must be resolved in order to
meet the goals and fulfill the mission of the organization or unit. Deliberations are
particularly important in the virtual arena of R&D work; it is through deliberations that the
work is moved forward.
This research project focused on three main areas of inquiry related to deliberations,
including the nature of optimal deliberations in virtual R&D organizations and projects,
factors that differentiate successful deliberations, and the impact of structure and
organization on the success of deliberations. It used Pava’s (1983) STS framework for
identifying and analyzing key choice points in nonlinear work processes to understand how
virtual work is coordinated across time and space in R&D settings where equivocality is
often high. Other research has also demonstrated the value of deliberation analysis to
identify sources of variances and delays in new product development (Shani and Sena,
2000; Tenkasi, 2000; Purser, 1992; Pasmore and Gurley, 1991). This research project was
designed to extend application of this empirical method into work settings where
equivocality is greater to help recognize how uncertainty shapes virtual projects and their
outcomes.
An extensive review of the literature in sociotechnical systems theory, virtual organization,
and organizational information processing helped frame the context and focal questions for
this research. Using the literature in these fields as background in developing the
protocols, a series of scoping interviews were conducted with each organization to gain an
understanding of the projects and the participants in the virtual work. Additional
structured interview protocols were then developed and applied in the actual research
process. Through a combination of structured interviews and observation, key
deliberations were identified and tracked in each research site in order to gather core data
about the process and outcomes of each. Finally, follow-up interviews, again relying on a
structured protocol, were conducted to assess the quality and outcomes of the deliberations
process.
It is important to understand the work context and collaboration demands of the work itself
in order to understand the complexity of managing virtual knowledge work in R&D
projects. STS provided a powerful lens through which to view the context within which
knowledge generation, expansion and sharing occurs across the R&D continuum; and it
highlights the importance of both social and technical systems in virtual work
environments.
STS theory focuses on examining the social and technical subsystems of work (Lipnack
and Stamps, 1997;Pasmore, 1988; Ackoff and Emery, 1972; Emery, 1959) to better design
effective work systems. Initially, STS was developed as a design methodology in
manufacturing and chemical processing industries (Davis and Cherns, 1975, Emery and
Thorsrud, 1969; Rice, 1953); the design principles (Cherns, 1976) developed from this
work served well as applied to routine work

6. 6
With the rapid development and expansion of the service sector in the 1980s, a greater
emphasis was placed on knowledge-intensive, non-routine work. Non-routine work
systems, like those found in R&D, involve nonlinear conversion flows, multiple and often
concurrent tasks that are highly interdependent, and a high degree of specialized expertise
and professionalism (Pava, 1983). For work in non-routine systems, 'deliberations'
became the primary unit of analysis (Taylor, Gustavson, & Carter, 1986; Pava, 1983).
Pava (1983) described deliberations as "equivocality reducing events" or choice points
relative to knowledge generation and use that are crucial to non-routine work systems. In
this study, each project provided a rich assortment of deliberations for us to analyze. For
example, in the Orchid project, choices had to be made about what experiments to run,
how to design those experiments, and how to make sense of the results. For the UDS,
choices had to be made about what data to include, what diagnostic instruments to use, and
who would have access to the data. In the LVG project, choices had to be made about
what new features to develop, what contractors to use, and what the requirements would be
for those contractors.
Using the STS framework for analysis of the three projects highlighted the challenges of
managing virtuality from both social and technical perspectives simultaneously. It has, in
particular, helped increase our understanding of how knowledge is created, disseminated,
and dispersed along a continuum of virtual R&D work in which the demand for “social”
coordination appears to increase with higher levels of uncertainty, and the demand for
“technical” coordination appears to increase as uncertainty decreases.
3. COORDINATING MECHANISMS
One of the key challenges in virtual work and knowledge generation and dissemination
arises from the need to manage interdependence among the work activities (Herbsleb,
2007; Gibson & Gibbs, 2006; Chudoba, et. al., 2005; Ramesh and Dennis, 2002; Malone
and Crowston, 1994). Coordinating mechanisms are developed or emerge because of the
need to manage this interdependence and avoid creating or exacerbating barriers that affect
the capacity to reduce equivocality in virtual organizations. In the knowledge work
environment of virtual organizations, several types of knowledge barriers often arise. Lack
of knowledge about the work, procedures and processes, or the capabilities of virtual
participants can slow or even derail progress. Even where it is available, there may be a
failure to utilize knowledge by participants. A third potential set of knowledge barriers
arises from a failure to share knowledge that is known to exist. Lack of a common frame of
reference can also become a significant knowledge barrier in virtual organizations. A
connection between coordination mechanisms and the possibility of mitigating knowledge
development barriers is based on theory of organizational information processing and
mutual meaning making (Malhotra & Majchrzak, 2004; Boland and Tenkasi, 1995; Weick,
1995, 1979; Daft & Lengel, 1986; Galbraith, 1974). It postulates that structural
mechanisms for coordination must provide the means to handle the amount and richness of
information processing required by the uncertainty and equivocality of an organization’s
tasks, interdepartmental relationships, and environment in order to co-create knowledge
(Boland and Tenkasi, 1995; Nonaka, 1994). Coordinating mechanisms can make a major
difference in how well deliberations in non-routine work incorporate the right information
and knowledge, and the right participants at the right time.

7. 7
Specific mechanisms to permit coordination have been proposed by Galbraith (1974) and
others using an information processing view of organization design, starting with goal-
setting, hierarchy, and rules, and extending to development of lateral relations to increase
the capacity to process information. In a similar format, Mintzberg (1998, 1983, 1979)
proposed a model with six broad categories of coordination: (1) mutual adjustment; (2)
direct supervision; and standardization of (3) skills, (4) work processes, (5) results, and (6)
norms. However, more specific to global software projects, and potentially most relevant
for our study of R&D work, Sabherwal (2003) condensed many classifications identified in
the information systems literature into a continuum of four main types of coordination
mechanisms: (1) standards; (2) plans; (3) formal mutual adjustment; and (4) informal
mutual adjustment.
Coordination through “standards” and “plans” relies upon pre-specification of rules,
routines, techniques, and targets. Both are mostly impersonal in nature once implemented
and appear to represent elements of the technical system in virtual organizations. In
contrast, both forms of “mutual adjustment” appear to be coordinated through
interpersonal communication, feedback and interaction, which are more representative of
the social system in organizations. In addition to defining these key modes of coordination,
much research has identified the level of task uncertainty and the degree of task
equivocality as key determinants of the suitability or requirement for specific coordination
mechanisms (Kraut and Streeter, 1995; Daft and Weick, 1984; Argote, 1982; Weick, 1979;
Van de Ven, Delbecq and Koenig, 1976; Galbraith, 1974; Thompson, 1967). According to
Sabherwal (2003), the proposition has been that “more informal, communications-
oriented” mechanisms (those that STS would suggest are within the social system realm)
are more suitable “when uncertainty is greater: during the requirements analysis phase”.
He also says that more impersonal, “more formal, control-oriented” mechanisms (those
that STS would suggest are within the technical system realm) are “most suitable when
uncertainty is less during the design, implementation, and testing phases of a project.”
This connection between the level of task uncertainty and the prevalence of specific types
of coordination mechanisms has been further validated for virtual organization. Sutanto,
Kankanhalli, and Tan (2011) found a “fit” between types of task dependence (with
increasing uncertainty) and optimal task coordination portfolios in global virtual teams.
Lower task interdependence (pooled or sequential) was primarily coordinated by standards
and plans, while higher task interdependence (reciprocal or team) required various forms
of mutual adjustment. Gassman and von Zedtwita (2003) found a similar correlation
between a set of determinants incorporating different levels of uncertainty and four distinct
forms of virtual team organization used to execute R&D projects in various multinational
companies. Extending these conclusions of prior studies to apply across the full continuum
of the R&D process including pure research (R1), one could expect a pattern of different
coordination mechanisms being predominant or primary for different R&D stages based
on the degree of uncertainty in the process (see Figure 2).

8. 8
Figure 2: Coordination across the Continuum of the R&D Process
4. FINDINGS
Initial data gathering through early interviews and secondary data sources about the nature
and operating process of each of the projects studied helped place each on the R&D
continuum. As has been noted, the Orchid project was a pure research effort, placing it at
R1 on the continuum. The UDS project was in the developmental stage, initially placed at
D1 and moving into D2 on the continuum. The LVG project included in the research had
one set of activities placed clearly at the start-up phase of the developmental stage (D3),
while another set of activities was clearly into the scale-up developmental stage (D4) on
the continuum. Once the position on the R&D continuum was determined for each project,
in-depth interviews and observation were used to gather data about the types of
deliberations that occurred in each site, about the types of knowledge barriers that impeded
the work, and about the coordination mechanisms the emerged or were developed to deal
with those knowledge barriers.
4.1 The Orchid Project
The Orchid project was an international multi-university collaboration by a team of 20
physicists and graduate students led by faculty at the California Institute of Technology
(Caltech) who partnered with scientists at other universities in Europe and North America.
The research was theoretical and experimental and required high interdependence among
physically dispersed teams. There were two primary teams actually designing and building
the equipment needed and conducting the experimental research, and three other groups
who formed a theoretical team to provide support for experimentation and for advancement
of optomechanical theory. All groups were well known, yet very few had ever worked
together before this project.

9. 9
Key deliberations within the Orchid project focused on the selection of experiments to run,
the design of the actual experimentation, and the interpretation and refinement of the data
gathered. Knowledge barriers associated with these deliberations were significant. The
disciplinary roots of the various groups were related but quite different. The groups used
different language to describe the same data, and each group had its own unique problem-
solving approach. A significant challenge was that the wide geographic dispersion in this
collaboration combined with the high degree of required reciprocal and team
interdependence between their disciplines and laboratory facilities carried a constant threat
of failure to utilize knowledge if the diversity of scientific perspectives could not be
accessed and integrated for creative problem-solving and interpretation in the experimental
process. Another major barrier to the acquisition of knowledge resulted from differences
in and incompatibility in the equipment used for experimentation. Further, all scientists
engaged in the Orchid project were also engaged in other demanding research projects and
priorities. There was a high potential for conflict in priorities or a failure to communicate
across the project, exacerbated by very tight timelines and 6-month review periods
administered by DARPA.
While there was a great deal of mutual respect and strong motivation to collaborate, this
project involved a tremendous challenge to invent a new methodology so the devices
created at Caltech could run on different experimental equipment in Europe. This co-
invention required a detailed understanding by each party of the other’s technical
capabilities and limitations, a very challenging task within a virtual organization.
Interestingly, the mechanism that most helped bridge these different frames of reference
was the role of an embedded researcher, a European graduate student who came to Caltech
for a short visit by chance and was able to see differences in approach between the
experimental groups and who recognized the need to merge the approaches. Another
graduate student, this one from the theoretical school, was also sent to Caltech and was
able to give real-time suggestions and quickly interpret experimental data for the theorists.
The liaison role that emerged served a clear purpose in helping to coordinate information
and knowledge exchange. This face-to-face connection provided by several students in the
project proved to be a vital coordinating mechanism.
4.2 The UDS Project
This project involved the design and ongoing maintenance of the Uniform Data Set (UDS),
a longitudinal database of clinical and neuropathological information on Alzheimer’s
patients in the United States sponsored by the National Institute on Aging (NIA). From
1984 to 1999, Rush Presbyterian Medical Center maintained a minimum data set related to
Alzheimer’s; over the years quality control of this data set suffered resulting in a missing
data rate of 20-30%. By 1999, the NIA, recognizing the need for a reliable and robust data
set as a key resource for Alzheimer’s research, established a National Alzheimer’s
Coordinating Center (NACC) at the University of Washington-Seattle. The Center’s
mandate was to encourage and support more effective collaboration among Alzheimer’s
Disease Centers throughout the United States in development and utilization of a Uniform
Data Set. Since then, the NACC has worked with a clinical task force of Alzheimer’s
Disease Center directors and clinical core directors to develop and update the standardized
content of the UDS.
Key deliberations in this project included the selection of data points to include in the data
set, an important issue because it determines what longitudinal information will be
available for researchers to use in their investigations of Alzheimer’s and related

10. 10
conditions. Another key deliberation revolved around how to collect the UDS data: the
UDS is based on a set of standardized instruments and the cost for using these instruments
has increased dramatically enough that there is now an effort to determine whether NACC
should create its own instruments. In addition to how to select the data to be included and
the means and frequency of data collection, another important deliberation focused on how
to distribute and use the UDS data for research, as well as who should have access.
The move to the UDS from the original data set raised a number of issues: initially, many
of the Alzheimer’s Disease Centers resisted the concept of a coordinating center and
viewed the requirement to use standardized data collection approaches as an imposition on
being able to collect data best suited to their particular research interests. This created
major barriers to knowledge sharing in the early deliberations about what elements to
include in the UDS. Other knowledge barriers have emerged from the different frames of
reference associated with the diverse disciplines among researchers. Data are collected
and analyzed by a multi-disciplinary group whose communication is further complicated
by dispersion of people across separate departments and buildings, yet there is occasionally
a failure to use the knowledge of one or more specific disciplines in deliberations.
The NACC itself was a purposefully designed coordinating mechanism by the NIA. It has
provided an infrastructure for effective deliberations on the design and ongoing refinement
of the UDS. Another important coordinating mechanism lies in the skill of specific
individuals in key roles within the NACC and NIA: these individuals have built
relationships and overcome barriers across organizational and disciplinary. Further, there
are organizational opportunities to meet face-to-face on a bi-annual basis, promoting
information sharing and knowledge dispersion.
4.3 The LVG Project
The Large Video Game project is a commercial product development process; it is based in
the US and has put together a virtual organization geographically dispersed across the
globe. There is limited economic viability for face-to-face interaction among members of
the virtual project teams. Production includes art assets, engineering, website design, and
overall quality assurance. In addition to LVG home-based staff, the virtual organization
includes external art asset vendors as well as engineering and website vendors. Most of
the vendors are quite familiar to LVG despite geographical distance, and the overseas
vendors are large, world-class facilities serving global clients.
Key deliberations at LVG involve the core choice points that occur at the front end of the
process: vendor selection is a deliberation of primary significance in all aspects of game
development. Other key deliberations include defining and estimating the outsourced
project work and specifying documentation and requirements. These deliberations are
conducted before the production process for a particular product begins. Deliberations in
virtual art production were different in nature from those for engineering and website, in
part because of the type of work being outsourced and the anticipated outcomes.
Key knowledge barriers were less evident in virtual art production than in all of the other
development work conducted through virtual organization at LVG. For virtual engineering
and web systems, significant barriers did arise. Issues included unclear expectations,
unrealistic timeframes, delayed data transfer, and lack of documentation. Knowledge
sharing barriers occurred in systems engineering because of intellectual property issues
whereby LVG core operations could not share vital source code with vendors. Lack of
planning meant that expectations about requirements often diverged and were not always

11. 11
effectively resolved between LVG and vendors. In deliberations about vendor selection,
there was an initial failure to utilize knowledge that other divisions had about reliable
capabilities and technical set-up in companies distributed across the vendor community.
Knowledge barriers associated with different frames of reference affected quality
assurance: different parts of the organization, for example, had different understandings
about the pace expected for work completion.
In the relatively routine and mature work processes of virtual art production for LVG, Inc.,
there are clear expectations about task deliverables, and agreements on acceptable output
are communicated and coordinated using screen shots, visual targets, emails, and extensive
digital documentation. Uncertainty is low and the primary coordinating mechanisms are
standards and rule based. For engineering and web development, however, there are a
number of other coordinating mechanisms that emerged and were applied. For this group,
staff often did not know the details of how the outputs were to be achieved, though they
did know what was expected. The virtual nature of the work reduced the ability for the
quick face-to-face feedback that is possible when co-located, making it difficult to quickly
correct misunderstandings or other knowledge frame of reference issues that arose. This
was particularly problematic in an environment where the delays and cost overruns that
resulted had immediate performance impact.
Many of the challenges of virtual work are related to an organization’s capacity to share
information and knowledge across time and space, and it is often assumed that time,
geography, and language will be significant barriers to successful virtual collaboration. It
was interesting to note that, for LVG in particular, the commonly accepted challenges
associated with time zone, language, and cultural differences proved to be relatively
insignificant barriers to sharing or using knowledge in key deliberations about expectations
and requirements for art production.
5. DISCUSSION AND CONCLUSIONS
To review quickly, the research project reported here was conducted in three organizations,
and focused on projects within each that were conducted in complex virtual organizations.
The research sites included a pure research project conducted by physicists and their
students around the world, a database development project to support Alzheimer’s
research, and a commercial production process that produced video games. Through
interview and observation, characteristics of the virtual environments were catalogued and
data were collected about the virtual work processes, the challenges facing participants in
these virtual environments, and about the coordination mechanisms used to resolve those
problems in order to co-create knowledge. These data were then analyzed to determine
what coordinating mechanisms were used to deal with an overcome the barriers to
knowledge development and dissemination that exist in virtual organizations.
Of the four main categories of coordination defined earlier (standards, plans, formal
mutual adjustment, informal mutual adjustment), all were utilized to some degree within
each of the three R&D projects in our study sample. Yet, the most significant mechanisms
in mitigating knowledge barriers and improving R&D performance in the context of virtual
organization varied across the R&D continuum.
The technical, or structural approaches to coordination were applied more in those
activities and projects with the lowest uncertainty: where desired outcomes had been

12. 12
clearly identified and defined, and where the methods to achieve the desired outcome were
clear, coordination by standards and plans seemed most effective. The social, or mutual
adjustment coordinating mechanisms were applied more in those activities and projects
where there was less certainty about outcomes and process: where the end state was not
well clarified and/or the methods by which to achieve them were unclear, coordination by
mutual adjustment appeared to be most effective. Thus, this comparative analysis of
virtual R&D projects confirms prior organizational theory and extends findings from
empirical studies of global software development to include the full continuum of the R&D
process.
In each of these virtual R&D projects, effective coordination has involved a specific
combination of elements and mechanisms. This is consistent with a knowledge-based
model of coordination applied earlier to a study of global software projects (Kotlarsky, van
Fenema, and Willcocks, 2008) where different types of coordination mechanisms were
found to make different contributions to knowledge sharing and development:
organizational/structural mechanisms facilitate knowledge flows; work-process
mechanisms make knowledge and expectations explicit; technology-based mechanisms
amplify knowledge; and the interpersonal skills and mechanisms associated with people
build social capital.
Analysis of the findings in this project show that there is a tendency for different categories
of coordination mechanisms (structural mechanisms and mutual adjustment mechanisms)
to play a primary role in managing different levels of task uncertainty and equivocality
related to the various stages of R&D. Structural coordinating mechanisms appear to be
more appropriate where uncertainty and equivocality are low. These mechanisms in the
three research sites included such practices as standardized skills training, process
standardization, common data formats, and standardized error-tracking procedures. The
mutual adjustment mechanisms appear most appropriate where uncertainty and/or
equivocality are high; these included formal elements such as temporary co-location, site
visits, informal meetings and impromptu communication as well as more formal
mechanisms such as designated liaison role, formal meetings and status reviews, steering
committees and task forces, and a formal facilitator role. Further, STS analysis of specific
deliberations within a pure research (R1) project and an exploratory development (D2)
project, when compared with deliberations in more structured start-up (D3) and scale-up
(D4) development work, provides additional insight as to why the categories of key
coordination mechanisms differ in different stages of R&D. In sociotechnical terms,
structural coordinating mechanisms are targeted to adjustments in the technical system,
and mutual adjustment coordinating mechanisms are more closely related to the social
system in organizations.
In general, then, our findings indicate that identifying the location of a virtual work project
on the R&D continuum can help practitioners anticipate the general nature and degree of
their coordination challenge and the mechanisms that are likely most important to apply.
Then, STS analysis of deliberations and knowledge development barriers can provide
detailed insights to inform the project-specific design of coordination for virtual R&D.
Finally, as suggested by the experience of our sample of case studies, including strategic
mechanisms that define common purpose for knowledge collaboration yields a framework
for coordination of distributed R&D work that reflects the thinking of the National Science

13. 13
Foundation VOSS program5
; namely, these virtual organizations are, and need to be
coordinated as open sociotechnical systems.
5
NSF Program Title: Virtual Organizations as Sociotechnical Systems (VOSS)

14. 14
REFERENCES
Ackoff, R., Emery, F.E., 1972. On Purposeful Systems. Aldine, Chicago. Ill.
Argote, L., 1982. Input Uncertainty and Organizational Coordination in Hospital
Emergency Units, Administrative Science Quarterly, 27, pp. 420-434.
Boland, R. J., & Tenkasi, R. V. (1995). Perspective making and perspective taking in
communities of knowing. Organization science, 6(4), 350-372.
Cherns, A., 1976. Principles of Socio-Technical Design, Human Relations, 29, pp. 783-
792.
Chudoba, K.M., Wynn, E., Lu, M., Watson-Manheim, M.B., 2005. How Virtual are we?
Measuring Virtuality and understanding its Impact in a Global Organization, Information
Systems Journal, 15, pp. 279-306.
Daft, R. L., Lengel, R. H., 1986. Organizational Information Requirements: Media
Richness and Structural Design, Management Science, 32, 4, pp. 554-571.
Daft, R. L., Weick, K. E., 1984. Toward a Model of Organizations as Interpretation
Systems, Academy of Management Review, 9, 2, pp. 284-295.
Davis, L., Cherns, A., (eds.) 1975. The Quality of Working Life, Vol. 2: Cases and
Commentary. Free Press, New York, NY.
Dixon, K.R., Panteli, N., 2010. From Virtual Teams to Virtuality in Teams, Human
Relations, 63 (8), pp.1177-1197.
Emery, E., 1959. Characteristics of Socio-Technical Systems. In: E. Trist, H. Murray (eds.)
The Social Engagement of Social Science, Volume II: The Socio-Technical Perspective.
University of Pennsylvania Press, Philadelphia, PA.
Emery, F.E., Thorsrud, E., 1969. Form and Content in Industrial Democracy: Some
Experiences from Norway and Other European Countries. Tavistock Publications,
London, UK.
Galbraith, J., 1974. Organization Design: An Information Processing View, Interfaces, 4,
3, pp. 28-36.
Gassmann, O., von Zedtwita, M., 2003. Trends and Determinants of Managing Virtual
R&D Teams, R&D Management, 33, 3, pp. 243-262.
Gibson, C.B., Gibbs, J.L., 2006. Unpacking the concept of Virtuality: The Effects of
Geographic Dispersion, Electronic Dependence, Dynamic Structure, and National
Diversity on Team Innovation, Administrative Science Quarterly, 51, pp. 451-495.
Herbsleb, J. D., 2007. Global Software Engineering: The Future of Socio-technical
Coordination, Proceedings of Future of Software Engineering, IEEE.
Kraut, R., Streeter, L., 1995. Coordination in Software Development, Communications of
the ACM, 38, pp. 69-81.

15. 15
Lipnack, J., Stamps, J., 1997. Virtual Teams: Reaching Across Space, Time and
Organizations with Technology. John Wiley & Sons, New York, NY.
Malhotra, A., Majchrzak, A., 2004. Enabling knowledge creation in far-flung teams: best
practices for IT support and knowledge sharing, Journal of Knowledge Management 8 (4),
pp. 75–88.
Malone, T.W., Crowston, K., 1994. The Interdisciplinary Study of Coordination, ACM
Computing Surveys, 26 (1), pp. 87-119.
Mashey, J. (2008, December 3). What can we learn from Bell Labs about managing energy
R&D? Available at http://bravenewclimate.com/2008/12/02/hansen-to-obama-pt-iv-
where-to-from-here/#comment-3023.
Mintzberg, H., 1979. The Structuring of Organizations. Prentice-Hall, Englewood Cliffs,
NJ.
Mintzberg, H., 1983. Structure in Fives: Designing Effective Organizations, Prentice-Hall,
Englewood Cliffs, NJ.
Mintzberg, H., 1998. Five Ps for Strategy, In: H. Mintzberg, J.B. Quinn, S. Ghoshal (eds.)
The Strategy Process, Prentice-Hall, NJ.
Nonaka, I. (1994). A dynamic theory of organizational knowledge creation. Organization
science, 5(1), 14-37.
Pasmore, W., 1988. Designing Effective Organizations: The Sociotechnical Systems
Perspective. Wiley & Sons, New York, NY.
Pava, C., 1983. Managing New Office Technology. Free Press, New York, NY.
Purser, R.E, Pasmore, W.A., Tenkasi, R.V., 1992. The Influence of Deliberations on
learning in new product development teams, Journal of Engineering and Technology
Management, 9, pp. 1-28.
Ramesh, V., Dennis, A.R., 2002. The Object-Oriented Team: Lessons for Virtual Teams
from Global Software Development, In: Proceedings of 35th
Hawaii International
Conference on System Sciences. IEEE Computer Society Press, Los Alamitos, CA.
Revkin, A. (2007, December 12), ‘R2-D2’ and other lessons from Bell Labs. New York
Times. Available at http://dotearth.blogs.nytimes.com/2008/12/12/r2-d2-and-other-lessons-
from-bell-labs/.
Rice, A.K., 1953. Productivity and Social Organization in an Indian Weaving Shed: An
Examination of the Socio-Technical System of an Experimental Automatic Loomshed,
Human Relations, 6, pp. 297-329.
Sabherwal, R., 2003. The evolution of Coordination in Outsourced Software Development
projects: A comparison of Client and Vendor Perspectives, Information and Organization,
13 (3), pp. 153-202.
Schweitzer, L., Duxbury, L., 2009. Conceptualizing and Measuring the Virtuality of
Teams, Information Systems Journal, 20, pp. 267-295.

16. 16
Sutanto, J., Kankanhalli, A., Tan, B., 2011. Deriving IT-Mediated Task Coordination
Portfolios for Global Virtual Teams. IEEE Transactions on Professional Communication,
54, 2, pp. 133-151.
Taylor, J.C., Gustavson, P.W., Carter, W.S., 1986. Integrating the social and technical
systems in organizations, In: D.D. Davis (ed.) Managing Technical Innovation. Jossey-
Bass, San Francisco, CA.
Tenkasi, R.V., 2000. The Dynamics of Cognitive Oversimplification Processes in R&D
Environments, International Journal of Technology Management, 20, 5/6/7/8, pp.782-798.
Thompson, J., 1967. Organizations in Action. McGraw-Hill, New York, NY.
Van De Ven, A., Delbecq, A., Koenig, R., 1976. Determinants of Coordination modes
within Organizations, American Sociological Review, 41, pp. 322-338.
Weick, K.E., 1995. Sensemaking in Organizations. Sage Publications, Thousand Oaks,
CA.
Weick, K.E., 1979. The Social Psychology of Organizing. Random House, New York, NY.