1. business and industrial

Discovering and Articulating what is Not Yet Known:
Using Action Learning and Grounded Theory as a
Knowledge Management Strategy
David J. Pauleen
School of Information Management
Victoria University of Wellington
New Zealand
email: david.pauleen@vuw.ac.nz
Brian Corbitt
School of Business,
Shinawatra University,
Thailand
Email: bcorbitt@shinawatra.ac.th
Pak Yoong
School of Information Management
Victoria University of Wellington
New Zealand
email: pak.yoong@vuw.ac.nz
Abstract:
This article presents a conceptual construct for the discovery and articulation of emergent
knowledge. The model is based on two widely accepted research methods, action learning
and grounded theory, and introduces the role of the organizational knowledge facilitator.
Essentially, the model allows organizations to gain practical and highly current experiential
knowledge from employees working in novel situations, including those using new
organizational processes and technologies. Such knowledge is often the source of
competitive advantage. The conceptual model is illustrated with a case on virtual team
leadership.
Key Words:
Knowledge, knowledge management, knowledge creation,
knowledge articulation, grounded theory, action learning
knowledge
discovery,
1
1.
Introduction
This paper describes a method for discovering and articulating experiential knowledge
learned by people working in novel situations, where knowledge is emergent, and
behavioural and cognitive models do not yet exist. Having a method that allows for the
discovery and articulation of what has been heretofore unknown provides an important tool
in organizational knowledge management strategies.
Novel work situations, including new work structures such as virtual teams, new processes
such as e-commerce, and new technologies such as networked mobile devices, are
increasingly found in global organizations leading to an increasing need to discover how
people can work effectively in such situations and with such technologies. In this article it is
argued that action learning provides a way to “discover” knowledge, while grounded theory
methods provides a way to “articulate” this knowledge in a practical and relevant manner.
The creation of the role of knowledge facilitators to work with people situated in novel
situations is proposed. The knowledge facilitators role is two-fold: to facilitate reflection
and learning in action learning situations and to use grounded theory methods to articulate
lessons learned in a form relevant to the wider organization.
This paper will demonstrate through the use of a case study on virtual team leadership how
action learning and grounded theory can work together in generating individual experiences
and from them create explicit knowledge.
In the first section the aspects of knowledge management, action learning and grounded
theory relevant to our proposed model will be introduced. A description of the role of the
knowledge facilitator as well as background information on virtual teams and virtual team
leadership will also be included. The second section of the paper will introduce the virtual
team leadership case, and the final section will discuss the implications and applications of
this method.
2.
The Role of Discovery and Articulation in Knowledge Management
Knowledge management is the systematic and explicit management of knowledge related
activities, practices, programs and policies within the enterprise (Wiig, 2000) to effectively
apply an organization’s knowledge to create new knowledge to achieve and maintain
competitive advantage (Alavi & Leidner, 2001).
The extant literature identifies two broad approaches to knowledge management, both are
structural and both heed little attention to the discovery and articulation of experiential
knowledge learned by people working in organizations. One focuses on the 'hard' aspects
such as the deployment and use of appropriate technology, the other focuses on the 'soft'
aspect, the capture and transformation of knowledge into a corporate asset. This second
approach includes the management of people and processes. These two approaches are
captured by Sveiby (2001) who defines two categorizations of knowledge management.
The first categorization of knowledge management is the management of information
(Sveiby, 2001). This approach views knowledge as objects that can be handled by
information management systems. The key goal of this approach is to increase access to
information through enhanced methods of access and reuse of documents through, for
2
example, hypertext linking, databases, and full-text search. Networking technology in
general (especially intranets), and groupware in particular, are key solutions. This approach
is based on the idea that technology harnessed to a great volume of information will make
knowledge management work.
The second categorization is the capture and transformation of knowledge into a corporate
asset through the management of people (Sveiby, 2001). This approach views knowledge as
a process - a complex set of dynamic skills and know-how that is constantly changing.
Commonly viewed as a management issue, approaches tend to focus more on innovation
and creativity, in the style of the "learning organization" as advocated by Senge (1990),
where organizational behaviours and culture also need to be changed. To make this
approach work, a "holistic" view is required, and often theories of behaviour of large-scale
systems are invoked. The aim here is to get people to share what they know. Processes are
what matter, not technology. This approach has yielded substantial research in social
learning as an enabler in organizations (Pascoe, Ali and Warne, 2002; Warne, Ali and
Linger, 2002). However the formal models created, architectures of social learning, are in
themselves simplified versions of what are essentially complex processes and do little to
assist us understand the process of discovering and articulating experiential knowledge
learned by people working in novel situations.
It is clear that knowledge management is not simply a matter of managing information; it is
essentially a deeply social process, which must take into account human and social factors
(Clarke & Rollo, 2002; Thomas, Kellog & Erickson, 2001), as well as cultural issues.
Thomas et al. (2001) argue that a successful knowledge management system is one that
includes a knowledge community, where people can interact in the discovery, articulation,
use and manipulation of knowledge. Nonaka and Toyama (2003) argue that the
organization is an entity that creates knowledge through action and interaction within the
organization and with its wider environment. There must also be a recognition that
knowledge needs to be discovered, captured and articulated in those organizations. Kluge,
Stein and Licht (2001) argue that dedicated techniques must consciously be used to make
knowledge management happen. They continue (Kluge, Stein and Licht (2001, p.11) that
“many of these techniques are well known – forming cross-functional teams or introducing
appropriate incentive schemes, for example – but their application, coordination and
alignment, as well as their detailed design make the difference between successful
knowledge management and an expensive project that not only fails, but is
counterproductive.”
Discovering or creating new knowledge, particularly knowledge that can be applied to
existing and new situations, is a fundamental goal of KM (Weiss, Capozzi and Prusak,
2004; Wiig, 2004; Bollinger and Smith 2001;Wiig, de Hoog and van der Spek, 1997; Wiig,
1997). In a world of non-stop globalization and technological change, new challenges and
situations spring up daily. Discovering or creating knowledge to meet these challenges is an
essential survival tool for organizations. New knowledge can drive innovation and increase
organizational effectiveness and productivity (Fahey, Srivastava, Sharon and Smith, 2001;
Quintas, Lefrere and Jones 1997; Demarest 1997).
However, it is recognized that much knowledge is tacit, either within individual’s heads
(‘know what’, experiences, skills, etc) or as social processes (‘know how’, ‘know why’,
shared experiences) (Bollinger and Smith, 2001). An important objective of KM continues
to be how to make tacit knowledge explicit. Ellerman (1999) argues that active learning is
an effective way for transferring tacit knowledge. Kolb (1976) argues that knowledge
3
created through transformation of experience can also be applied to organizations. Kluge,
Stein and Licht (2001) argue that understanding the nature of knowledge alone and
embedding practice in organizations will assist in managing knowledge, but their model like
so many before inevitably induces a structuralist solution reverting to the analogy of the
orchestra conductor, which avoids the real methodology behind the capture and articulation
of knowledge.
Of existing knowledge management models, the most widely accepted is that of Nonaka’s
SECI model (Nonaka, 1994), which has been universally acclaimed and used by many
organizations worldwide as a starting point for knowledge management practices. The SECI
model deals in part with the conversion of tacit knowledge to explicit knowledge through
processes of socialization. However, Glisby & Holden (2003) assert that the Japan-specific
cultural constraints tacitly embedded in the model mean that it is not possible for it to be
successfully transferred to Western nations. They claim that in order for the model to work
in a Western setting, it might be necessary to first introduce Japanese values and
management techniques which would embed the require cultural conditions necessary for
success of the model.
Might there be another way in which organizations in a world of constant change can make
tacit knowledge explicit in culturally appropriate ways? Figure 1 illustrates the challenge.
New
Situation
Process of
Discovery
Process of
Articulation
Explicit
Knowledge
Figure 1: How can individual and shared tacit knowledge be made explicit?
In this paper, it is argued that two ‘tried and true’ knowledge-generating methods, action
learning and grounded theory, can work together to meet this challenge and become an
important tool in organizational KM.
2.1
Discovery – Action Learning
The term action learning was coined by Revans (1982: 626-627) and is defined as “a means
of development, intellectual, emotional or physical, that requires its subjects, through
responsible involvement in some real, complex and stressful problem, to achieve intended
change to improve their observable behavior henceforth in the problem field.”
Action learning is a practical group learning and problem-solving process where the
emphasis is on self-development and learning by doing. The group, known as the action
learning 'set', meets regularly and provides the supportive and challenging environment in
which members are encouraged to learn from experience, sharing that experience with
others, having other members criticize and advise, taking that advice and implementing it,
4
and reviewing with those members the action taken and the lessons that are learned
(Margerison, 1988).
Marsick and O’Neil (1999) identify three different ‘schools’ of thought on action learning:
Scientific, Experiential and Critical Reflection. Table 1 provides a summary of the
theoretical background to each school of thought.
School
Influenced by
Scientific
R.
W.
(1982)
Theoretical background
Revans Action learning is viewed as a model of problem solving in three
stages:
1.
2.
3.
System Alpha - design of a problem solving strategy including a
situation analysis.
Systems Beta – the negotiation of the strategy including survey,
hypothesis, experiment, audit and review.
System Gamma – the learning process associated with the
strategy.
Experiential
D. Kolb (1984)
Based on Kolb’s experiential learning cycle, proponents of this
school advocate that the starting point for learning is action followed
by reflection on action, preferably with the support of other group
members. Any further action should focus on changing previous
patterns of behaviours.
Critical
Reflection
J. Mezirow (1990, Proponents of this school see ‘reflection on action’ as a necessary but
1991)
insufficient condition for learning. They believe that participants
should also go deeper and examine the assumptions and beliefs that
influence their practice. Reflection at this deeper level focuses a
participant’s attention on the root of the problem and transform
previously held perspectives of the same problem.
Table 1: The Three ‘Schools’ of Action Learning
(Adapted from Marsick and O’Neil, 1999; pp. 161-163)
Marsick and O’Neil uncovered two themes that are common to all three schools of action
learning and that is, the group participants: (a) meet on equal terms and (b) are engaged in
understanding and solving unstructured problems where there are no one right solution.
The group of four to six participants, known as the action learning 'set', meets regularly and
provides the supportive and challenging environment in which members are encouraged to
learn from experience, sharing that experience with others, having other members criticise
and advise, taking that advice and implementing it, and reviewing with those members the
action taken and the lessons that are learned (Margerison, 1988). Many learning sets
require the assistance of a ‘learning coach’ and the role of the coach depends on (a) whether
the learning set works on one project as a team or the participants work on individual
projects and (b) the level of facilitation on group process (Marsick and O’Neil, 1999).
In many respects, an action learning set is a community of practice (CoP) as the learning set
exhibits the three dimensions of a CoP (Wenger 1998): a group who interact and share ideas
(community), share a common area of interest (domain), and develop many areas of
5
common professional practices that relate to the domain of interest (practice). However, the
key difference is that not many CoPs have a focus on problem-solving that action learning
sets do.
2.2
The ‘experiential’ school of action learning
Many proponents of action learning advocate that ‘learning’ is the main reason for their
program and promote Kolb’s experiential learning cycle as its theoretical base (Marsick and
O’Neil, 1999). Kolb’s (1984) model of experiential learning suggests that learning is a
dialectic and cyclical process consisting of four action and reflection stages as shown in
Figure 2 and briefly described in Table 2:
Active
experimentation
Concrete
Experience
Abstract
conceptualization
Reflective
observation
Figure 2: Kolb’s Model of Experiential Learning
Concrete experience
The learners identify their own learning activity and
involve themselves fully and openly in a new experience
Reflective observation
The learners observe, analyse and reflect on the new
experience
Abstract conceptualisation
The learners create concepts that integrate
observations into contextually relevant models
Active experimentation
The learners apply these models in unfamiliar situations
the
Table 2: Kolb’s Model of Experiential Learning
In practice, this cycle of action and reflection activity does not flow in a linear and
sequential fashion. It is far more fluid and dynamic and the learners move back and forth
6
among the stages. The experiential learning model provides a useful guide to learning and
progress through the process.
In many respects, Kolb’s description of experiential learning appeals in that learning to be a
leader of virtual teams requires more than just 'reading', 'talking' and 'thinking' about it. It
also requires the actual experience of 'doing' it. The combined efforts of action and
reflection provide the essential processes for enabling virtual leaders to gain the skills and
insights for managing and supporting those parts of virtual team leadership which are
uncertain and unpredictable. Leaders need to know what they can or cannot do before
embarking on improving or changing these leadership behaviours. This link between what
is already known - the leaders' experience in conventional teams - and what they want to
know, change or improve - the use of the electronic meeting tools - is also a common
feature of experiential learning. The process of integrating new experience with past
experience through reflection is an important aspect of the trainees’ learning to be leaders of
virtual teams.
2.3
The role of reflection
In Reflection: Turning Experience into Learning, Boud et al. (1985) describe the essence of
reflection in experiential learning as "reflection is a form of response of the learner to
experience. In the proposed model, in the stage, Process of Discovery, there are two main
components: the experience and the reflective activity based upon that experience . . . (that
is) after the experience there occurs a processing phase; this is the area of reflection.
Reflection is an important human activity in which people recapture their experience, think
about it, mull it over and evaluate it" (pp. 18-19).
Boud et al.'s description of 'reflection' is similar to Schon's (1987) notion of 'reflection-onaction' which is "thinking back on what we have done ..." (p. 238). Schon was also
concerned with what he called the "crisis of confidence in professional knowledge ... and
professional education" (p. 8) and believed that this crisis was caused by the "prevailing
epistemology of practice" (p. 12) in professional education institutions. He based this
argument on the assumption that professional expertise and thinking should not only depend
on the application of established theory on particular situations but also on experience-based
knowledge and on non-logical kinds of thinking about what is relevant and appropriate in
the context of those situations.
To enhance this latter kind of professional thinking, Schon (1983) suggests a reflective
approach to professional education based on the notion that "in much of the spontaneous
behaviour of skilled practice we reveal a kind of knowing which does not stem from a prior
intellectual operation" (p. 51) but on a reflective practice process called 'reflection-in-action'
which is thinking about the action while one is doing it, rather than after the event. Citing
his own experience of building a gate, he explains: "In the midst of action, I invented
procedures to solve the problem, discovered further unpleasant surprises, and made further
corrective inventions ... reflection on each trial and its results sets the stage for the next
trial" (p. 27). Boud and Walker (1993) extended Schon's notion of 'reflection-in-action'
which they described as "reflection which takes place during the event" (p. 76).
In summary, the Process of Discovery requires those involved in a new situation to engage
in a simultaneous process of action and reflection. This process allows participants to learn
and apply these lessons as they work within a new context and encounter novel situations.
7
However, as indicated by Schon’s statements, it is unlikely that most professionals have the
experience and skills to engage in reflective action. This is why in this model the role of the
knowledge facilitator is introduced, part of whose job is to facilitate the discovery of
individual and organizational learning and knowledge development.
2.4
Articulation – Grounded Theory
Grounded theory in an inductive process, in which concepts, insights, and understanding are
developed from patterns in the data (Yoong & Pauleen, 2004). It is this inductive process
that allows for the development and articulation of theories or models in situations where
little previous experience or knowledge exists.
In traditional grounded theory data is systematically gathered and analysed until theory
emerges during actual research, doing so through the continuous interplay between analysis
and data collection (Strauss & Corbin, 1990). Central features of this analytic approach
include the general method of (constant) comparative analysis, theoretical sampling,
theoretical sensitivity and theoretical saturation (Glaser & Strauss, 1967). Strauss and
Corbin later introduced a paradigmatic framework to assist in structuring data in meaningful
ways (Strauss & Corbin, 1990). Recently, researchers have used grounded theory methods,
particularly coding techniques, to accomplish much the same as traditional grounded theory,
but in an arguably less rigorous way.
Grounded theory methods are highly congruent with the need to understand rapidly
evolving information systems as they are used in their organizational environments. Two
distinct characteristics of grounded theory are especially relevant. The first is that the
conceptual framework is generated from the data rather than previous studies, and the
second that the researcher attempts to discover the dominant processes in the social setting
rather than describing the unit under study. (Stern, 1987, p.81-82).
The choice of grounded theory as a method for the analysis and articulation of raw
experience is supported in situations where there is little previous research in an area, when
the focus is on human experience and interaction, when there is a high degree of
applicability to practice, and when there is a need for contextual interpretation. (Yoong,
1996, p. 33-35):
In summary, in situations where very little is known about the issues facing participants,
and where great amounts of data are primarily gathered in an unstructured format, grounded
theory provides a method for analyzing and articulating the data in ways - theory, models –
that are practical and relevant to the situations in which the data emerged.
2.5
The role of the knowledge facilitator
In both action learning and grounded theory, someone, usually a researcher, plays a critical
role. In action learning the role is to facilitate the learning, particularly the tacit learning, of
those involved in the action learning set. Often, this role will also involve some level of
teaching or training or perhaps providing outside expertise or perspectives to those involved
in the action learning set. This is a crucial role. Much learning follows reflection and
discussion with others and it is the role of the facilitator to encourage these. Schwarz (1994)
suggested that the facilitator should be acceptable to all members of the group, be perceived
8
as neutral, and carry no decision-making authority that could affect the members of the
groups in any adverse way.
In grounded theory, the researcher is the one to analyse and articulate the data into a form
that relates to the situation under study. Since grounded theory is primarily used in social
settings, the final analysis and articulation must be relevant to those in the social setting, i.e.
the theory or model that is developed from the data must have local relevance.
In this paper it is proposes that this is the role of organizational knowledge facilitators, who
have two primary responsibilities. The first is both to facilitate reflection and learning in a
designated organizational context, such as a team project or the introduction and use of new
technology. Probst (2000) explained this role as one where a trained facilitator can 'tease'
out the team leader's and members' accumulated knowledge and experiences. According to
Roth (2003), the knowledge facilitator can assist team members to collectively reflect and
create knowledge at the present time. The second responsibility is to collect, analyse and
articulate the experiences and learning that are generated in a form relevant to the wider
organization.
3.
Background to the Illustrative Case: Virtual Teams and Virtual
Team Leadership
In the second part of this paper, an action learning/grounded theory case involving virtual
team leaders is presented. This case illustrates the contention that action learning and
grounded theory are particularly relevant for discovering and articulating knowledge in
novel organizational situations that involve emerging organizational forms as well as new
technology. It is argued that virtual teams and virtual team leadership meet these criteria.
Townsend, De Marie and Hendrickson (1998:18) define virtual teams as “groups of
geographically and organizationally dispersed co-workers that are assembled using a
combination of telecommunications and information technologies to accomplish an
organizational task”. Virtual teams represent a new way of doing things in organizations.
They allow timely access to far-flung talent, knowledge and expertise. According to
Maznevski & Chudoba (2000) global virtual teams are often assigned the most important
tasks in an organization, including multi-national product launches, negotiating mergers and
acquisitions among global companies, and managing strategic alliances. However, the use
of virtual teams has outpaced our understanding of their dynamics and unique
characteristics (Cramton & Webber, 2000).
There has been long and extensive research on leadership in collocated teams and groups (a
1985 study counted over 300 definitions of leadership (Bennis & Nanus, 1985), there has
not been much empirical research on virtual team leadership. Studies (Hiltz & Turoff, 1985;
Hiltz et al., 1991) suggest the critical role leadership plays in groups working via
information and communication technology, and the importance of leadership in virtual
teams is noted in the practitioner literature (Lipnack & Stamps, 2000; Duarte & TennantSnyder, 1999; O’Hara-Devereaux & Johanson, 1994). Recent research (Kayworth &
Leidner, 2001-2002) has begun to look at leadership issues in virtual teams. These studies
suggest leadership qualities and practices that can lead to effective virtual teams and make
the case that the trend toward physically dispersed work groups
9
At the time the research behind this case was done (1997-99), virtual teams were relatively
new and their characteristics not yet understood The need existed to understand what virtual
team members and leaders needed to know to function effectively. This need is still
apparent today.
3.1
The Case
The case presented here was developed from a larger three-year study of virtual teams by
the first author1. The use of this case in this paper is to highlight how action learning was
used to generate experiential knowledge and grounded theory was used to articulate that
knowledge Specifically the case demonstrates the kinds of insights that can emerge when
employees are given the time and space to reflect on their work experiences. These insights
are potentially very valuable to organizations and their knowledge management programs.
This case focuses on the experiences of a learning set of professional business people in
New Zealand as they planned for and led their virtual teams within the larger context of
their individual organizations and the rapidly evolving ICT environment. The action
learning research methodology, central to the knowledge management-training model to be
introduced in this paper, will be explained briefly. The articulated conceptual model,
Building Virtual Relationships, which developed out of this study, is also briefly explained.
3.2
The Appropriateness of Using this Method in Understanding Virtual Teams
Since the researcher did not have ready access to research participants who had relevant
virtual team leadership experiences to provide the research data for the studies, an Action
Learning training program (AL program) was designed to attract research participants for
the purpose of data generation. The AL programs provided an environment in which busy
professionals working with new technologies in new ways could receive knowledge and a
safe place to improve their virtual team leadership skills. Table 3 shows the content of the
AL Program.
Session One
Virtual Team Implementation and Project Planning
Session Two
Developing Virtual Team Purpose, Communication
Strategies and Protocols, and Technology
Session Three
Developing Team Identity, Building Relationships
and Intercultural Communication Issues
Session Four
Preparing for and Facilitating Virtual Meetings
Session Five
Concluding a Virtual Team and Other Training Issues;
Virtual Teams in the Organization
Table 3: Outline of Virtual Team Action Learning Program
1
The findings of the full study have been presented in a previous paper (Pauleen, 2003-04).
10
One key factor influenced the adoption of action learning for this study. This was the desire
to base the studies on the practical experiences of professional practitioners. The researcher
believed in making the research relevant and had a strong commitment to engage in
research projects that had practical implications for business organizations.
The design of the AL program was both guided by the following set of principles in which
the virtual team leaders were encouraged to learn in groups and to use the learning groups
to:
• work and gather data on real issues and problems associated with virtual team
leadership,
• reflect and improve on their skills and knowledge
• interlink their action and reflection,
• share their action and reflection with others, and
• create and sustain a supportive and challenging community of critically informed
virtual team leaders.
(Adapted from Revans, 1982; Margerison, 1988)
Action learning has an iterative cyclical nature often involving the same learning group. The
learning group continues in successive cycles until an appropriate level of self-development
and learning is achieved. In this study, each iterative cycle involved a new learning group.
This is a modification of the action learning approach and was made to improve data
collection by accommodating the grounded theory notion of theoretical sampling. It should
be pointed out that each action learning training program was evaluated at the end of each
cycle and changes were made to the training program before the next cycle. As for the
participants, although their involvement with their action learning set ended at the end of
each cycle they were invited to get in touch with us if they wanted to discuss new
experiences or insights.
In this case study, action learning provided an ideal approach for the participants who were
in the process of unravelling the nature and complexity of virtual team leadership. The AL
program developed for this case provided an appropriate framework for studying virtual
teams and virtual team leadership.
3.3
The Study
This study investigated the question: How do facilitators of virtual teams build relationships
with their virtual team members? A pilot program involving one participant-facilitator and
two action learning programs, involving three participant-facilitators each, were conducted
in this study. Each training program was ten weeks long. The content of the program
covered virtual team issues and processes of concern to a virtual team leader (Table 3).
During the program, each participant planned for, evaluated the use of, or actually initiated
and facilitated a virtual team within their own organizational context. The three participants
and the researcher/facilitator in each program met every two weeks for two hours.
11
3.4
Collecting and Analysing Data from the Program
This section begins with a discussion of some field work issues associated with the study,
followed by a description of the procedures, with examples, used during the collection,
analysis and interpretation of the research data.
3.4.1
Field Work Issues
The field work in this study was divided into three blocks of activity, involving a pilot
project and the two training programs. Dividing the study into blocks of activity proved to
be a useful approach. The extended period between each block of fieldwork provided time
for transcription and analysis of the interview data. Equally importantly, these in-between
periods were used for reflection, interpretation and strategy building. These reflective
periods, which are built into the action research cycle as well as the grounded theory
method (Yoong, 1996) significantly influenced the way the next period of fieldwork was
conducted.
The following example from this case study is illustrative: the interim results from the first
training program helped the researcher to determine the selection of the second program
participants based on the principle of theoretical sampling. Participants in the second
program were selected because of their differences to those from the first training program,
both in their experience with virtual teams and in the global nature of their virtual teams and
team projects. As a result, the researcher was able to compare and contrast the emerging
theory with the data as prescribed by the constant comparative method.
3.4.2
Data Collection and Analysis
As discussed in an earlier section, the constant comparative method provides the researcher
with an established set of procedures for conducting the data analysis. Several methods of
data collection were used, primarily based on semi-structured interviews and discussions
between the researcher and the participants and informal participant reports, the researcher’s
journal, participants’ notes, and organizational documentation. Copies of electronic
conversations (i.e. e-mail) were also used. These different methods provided for the
collection of diverse kinds of data and enhanced the use of the constant comparative method
(Glaser & Strauss, 1967).
Semi-structured interviews were conducted with the participants during each of the AL
sessions. The list of questions in the interview guide was flexible and “not a tightly
structured set of questions to be asked verbatim as written … it is a list of things to be sure
to ask about when talking to the person being interviewed” (Lofland & Lofland, 1995: 85).
This flexible and guided conversation then flowed on from the initial leading questions. The
grounded theory principle of theoretical sensitivity also guided this conversation as the
researcher was looking for similarities, differences and density in the virtual team leaders’
accounts of their experiences.
As is common in qualitative research, a large volume of data was collected in both studies
(Gopal & Prasad, 2000), and the researcher began to analyse the data by listening to and
transcribing each recorded interview and discussion. Transcripts were returned to the
participants for member checking and validation.
12
The first step in the analysis of the data was to code all the interview transcripts as well as
the other relevant documents. Open coding techniques, a process of labelling the events and
ideas represented in the data (Baskerville & Pries-Heje, 1999) were used first: often this
involved assigning multiple codes (Table 4). Most of the data was coded using NVIVO, a
computer software program developed for use with qualitative research methods (Richards
& Richards, 1994). The transcripts were perused and each line, sentence or paragraph, were
assigned one or more conceptual codes, most often in terms of properties and dimensions.
As a grounded theory researcher, the data was approached without any particular
preconceived notion or framework (Trauth & Jessup, 2000) and simply assigned a
descriptive label.
Participant Comment
The other thing is working across
organizations. In the future we’re going to
increasingly be working across organizations,
even virtual organizations.
I would rather send an e-mail then use the
telephone, simply because of the amount of
work I am doing.
So I guess it’s an idea of the rolling present.
For example if you were to check your e-mail
four times a day and somebody else checks it
once every four days, you are going to
develop different concepts of work flow or
work pacing. Your contribution to the team is
different and you’ll probably judge other
people, the other team members, by your the
way you were doing it and the way you’re
accessing the team….
Conceptual Codes
Organizational Issue,
Culture
E-mail,
Communication Strategy,
Organizational Issue
E-mail,
Communication Protocols
Table 4: Examples of Assigning Multiple Codes
Based on similarities or differences, as well as emerging relationships, codes were grouped
into clusters of conceptual codes, called conceptual categories, representing a higher level
of abstraction (e.g. Figure 3).
Economic Barriers
Time Constraints
Psychological (loss of control)
Non-Technical Barriers
Cultural Barriers
Trust & Credibility
Time Differences
Organizational Diplomacy
13
Figure 3: Grouping Conceptual Codes into Conceptual Categories
Nine conceptual categories were eventually developed (Table 5). Extensive writing and
modelling around these categories were done. By nalysing the data from a variety of
perspectives — transcripts, coding, case studies, and integrative memos — it became
apparent that newer and higher levels of abstractions and relationships were forming.
Conceptual Categories
Communication Channels
Communication Strategies
Communication Protocols
Virtual Team, Leadership and
Related Issues
Culture
Human Interaction
Organizational Issues
Non Technical Barriers
Technology
Table 5: Key Conceptual Categories
As suggested by Baskerville and Pries-Heje (1999) grounded theory notation such as
memos and diagrams were used. A set of emergent models based on the codes and
categories that were taking shape were created, as well as the researchers’ intuition guided
by increasing levels of theoretical sensitivity. The researcher also wrote narrative,
chronological case studies of each of the participants. This gave him another lens through
which to view the data and to draw cross linkages between the experiences of each of the
participants. These cases also provided a valuable way to engage in ‘member checking’
with the participants when they read through them and verified their experience.
The data collection and analysis processes were repeated for the second AL program,
comparing emerging categories with those from the previous cycles. The notion of
‘theoretical sensitivity’ is particularly useful at these stages. It is “the attribute of having
insight, the ability to give meaning to data, the capacity to understand, and capability to
separate the pertinent from that which isn’t” (Strauss & Corbin, 1990: 42-43). This
sensitivity can be achieved by a variety of approaches including extensive literature search
in related fields of study and a series of reflections on personal and professional experience.
Any further data collection and analysis become more selective and are guided by the
emerging theory and a process known as ‘theoretical saturation’. This means that the entire
process continues until no additional data, coding, or sorting contribute to the extension of
the theory.
3.5
Case Outcome: Virtual Team Leadership
The major outcome of this study is a Grounded Theory of Virtual Leadership that made
explicit the issues facing virtual team leaders as they implemented and led virtual teams
(Pauleen, 2003-04). A specific outcome of the study was a model of how virtual team
leaders build relationships with their virtual team members. This model (Figure 4) includes
a unifying framework of three inter-related theoretical steps: Assessing Conditions,
14
Targeting Level of Relationship, and Creating Strategies. This study was the first to identify
the steps a virtual team leader undertakes when building relationships with virtual team.
Task to be undertaken by virtual
team
Leader undertakes:
Assessing
conditions
Targeting level of
Relationship
Creating
Srategies
Engaging in
task work
Factors present at
the initiation of a
virtual team
Team Issues
Boundary Crossing
Organizational Policies
Technology
Level of personal
relationship a leader
might choose to develop
with a team member
(low, medium, high)
Selection and use of
appropriate:
- Communication Channels
- Messages
Figure 4: The Three Steps in Developing Virtual Relationships (in organizational
context) (Pauleen, 2003-4)
This model has been described in detail in previous publications (see Pauleen, 2003-04).
Here it is just briefly summarised.
In Assessing Conditions, the leader considers all the factors present when a virtual project or
task is undertaken. Any number of factors, based on a variety of circumstances, may be
present. Based on a grounded analysis, these factors have been classified as Team Issues,
Boundary Crossing, Organizational Policies and Resources, and Technology. It is important
that the leader carefully assesses the likely impact of the factors present at the initiation of
the virtual team in order to have enough information to successfully complete steps two and
three.
The next step in the process of developing virtual relationships for the leader is Targeting
Level of Relationship. Level of Relationship can be defined as the ‘level’ of personal
relationship that the leader thinks is appropriate to develop with a team member to
accomplish the project goal or task. The leaders in this study described at great length the
kinds of relationships they felt were necessary to develop with their virtual team members,
given the conditions present at the start of the team. Based on these descriptions, three
15
different levels of relationship — low, medium and high — were defined. Targeting a level
of relationship is an important cognitive element in the relationship-building process. Not
only does it reflect the conditions present at the start-up of the team, but also the leader’s
understanding, based on personal experience, of how ‘close’ s/he needs to be with team
members in order to improve the chances of the team’s accomplishing its goals.
Successfully targeting the appropriate level of relationship requires experience and
consideration.
The third step in the process of developing virtual relationships is Creating Strategies. The
aim of Creating Strategies is to achieve the targeted level of relationship. Creating
Strategies takes into account the virtual context and involves the selection and use of
appropriate communication channels and message content, followed by the implementation
and management of relationship-building strategies. The selection of appropriate
communication channels is based on the conditions discussed in step one, that is,
availability and compatibility of channels, cultural or organizational preferences, team
member training and skills, etc. The selection of appropriate messages is based primarily on
the level of personal relationship chosen in step two, but may also take into account
conditions from step one, for example, a team member’s cultural preference for formality.
Implementing and managing the strategies the leader has created is the final part of the
relationship-building process, and this is ongoing. It begins with the implementation of the
leader’s chosen strategy. If the strategy the leader has created is the correct one, and the
targeted level of relationship is developed, then the relationship-building process has been
successfully completed and the project or task may begin, with the leader continuing to
manage and maintain the relationship as necessary. In a complex, long-term project, where
a higher level of personal relationship is desirable, the strategy created may include
continuous relationship building, taking place concurrently with project or task
implementation. However, should the relationship-building strategy fail in its desired
outcome, or should conditions change or new conditions come to light, then the
relationship-building process may need to be revisited. Should new members join the team,
the leader will need to repeat the process with each new member.
4.
Discussion and Implications of the Case and the Use of Action
Learning and Grounded Theory in Discovering and Articulating
Knowledge
The Three Steps in Facilitating Virtual Relationships model represents the experiences and
insights of a group of virtual team leaders and explains how they built relationships with
their virtual team members. Although derived in a local setting from a limited number of
leaders, the model provides practitioners (virtual team leaders) with a cognitive model of
how relationship building with virtual team members can be approached. This model
represents new explicit knowledge. It was ‘discovered’ and ‘articulated’ using a
combination of action learning and grounded theory (Figure 5). Versions of this model have
now been adopted for use in organizational training by Berlitz International Training and
Business Development.
16
New
Situation
Process of
Discovery
Process of
Articulation
Explicit
Knowledge
Virtual
Team
Leadership
Action
Learning
Grounded
Theory
Methods
Relationship
Building
Model
Application of explicit knowledge
Figure 5: Relevance of AL and GT Approach to KM
Using the action learning approach and facilitated learning and reflection, the virtual team
leaders in this study were able to gain valuable experience in the implementation and
management of virtual teams while the researcher was able to articulate an explicit model of
relationship building between virtual team leaders and members. Using a skilled
knowledge facilitator and similar approaches, it is reasonable to expect that organizations
could discover and articulate valuable knowledge about novel situations in which they and
their employees increasingly find themselves in (Figure 6).
The Role of
Knowledge
Facilitator
DISCOVERY
ARTICULATION
Facilitate
reflection and
learning of
those working
in novel
situations
Collect data and
using grounded
theory techniques
develop relevant
and practical
models
EXPLICIT
KNOWLEDGE
Incorporate
models back into
the organizational
learning program
Figure 6: The Role of the Knowledge Facilitator
Obviously, a key to success in this model is an organization's willingness to factor in
'reflection and learning’ as a critical operational part of any novel situation and to the
provision and use of trained knowledge facilitators, who should be trained communicators
specializing in facilitating learning, as well an relevant techniques in inductive analysis.
Organizations may want to look toward trained academics to pilot such programs within
their organizations.
To conclude, in the research case that underpins these recommendations it becomes
apparent that facilitated action learning and grounded theory enables dedicated reflection
and learning to be formalized and patterned in ways that have the potential to create
knowledge management practice at the individual, team, and organizational levels. By
targeting novel situations, particularly those involving technology and global interaction,
organizations may gain significant competitive advantage through the discovery,
articulation and application of otherwise unrevealed knowledge.
17
References
Alavi, M. & Leidner, D. E. (2001) Review: Knowledge management and knowledge
management systems: conceptual foundations and research issues. Management
Information Systems Quarterly, 25, 1, 107 – 136.
Baskerville, R. & Pries-Heje, J. (1999) Grounded action research: a method for
understanding IT in practice. Accounting Management and Information Technologies, 9, 123.
Bennis, W.G., & Nanus, B. (1985) Leaders: The Strategies of Taking Charge, Harper
Collins, San Francisco.
Bollinger, A. S., Smith, R.D. (2001) Managing organizational knowledge as a strategic
asset. Journal of Knowledge Management, 5, 1, 8-18.
Boud, D., Keogh, R., & Walker, D. (1985) Reflection: Turning Experience into Learning,
Kogan Page, London.
Boud, D. & Walker, D. (1993) Barriers to reflection on experience. In Using Experience
For Learning, Boud, D., Cohen, R., and Walker, D. (eds.), pp. 73-86. SRHE and Open
University Press, Buckingham.
Clarke, T. & Rollo, C. (2001) Corporate initiatives in knowledge management. Education
and Training, 43, 4/5, 206-214.
Demarest, M. (1997) Understanding knowledge management. Long Range Planning, 30,
374-84.
Duarte, N., & Tennant Snyder, N. (1999) Mastering Virtual Teams: Strategies, Tools, and
Techniques that Succeed, Jossey-Bass Publishers, San Francisco.
Ellerman, D.P. (1999) Global institutions: Transforming international development agencies
into learning organisations. The Academy of Management Executive, 13, 1, 25-35.
Fahey, L., Srivastava, R., Sharon, J., & Smith, D. (2001) Linking
business and operating processes: The role of knowledge management. IBM Systems
Journal, 40,4, 889-907.
Glaser, B., & Strauss, A.L. (1967). The Discovery of Grounded Theory: Strategies for
Qualitative Research, Aldine De Gruyter New York.
Glisby, M. & Holden, N. (2003) Contextual constraints in knowledge management theory:
The cultural embeddedness of Nonaka's knowledge-creation company. Knowledge and
Process Management, 10, 1, 29-36.
Gopal, A. & Prasad, P. (2000) Understanding GDSS in symbolic context: shifting the focus
from technology to interaction. Management Information Systems Quarterly, 24, 3, 509545.
18
Hiltz, S.R.,& Turoff, M.(1985) Structuring computer-mediated communication systems to
avoid information overload. Communications of the ACM, 28, 7, 680-689.
Hiltz, S.R, Dufner, D., Holmes, M., & Poole, S. (1991) Distributed group support systems:
social dynamics and design dilemmas. Journal of Organizational Computing, 2, 1, 135159.
Kluge, J., Stein, W., & Licht, T. (2001) Knowledge Unplugged, Palgrave, UK.
Kolb, D. (1976) Experiential Learning, Gower Publishing, Englewood, NJ.
Kolb, D.A. (1984) Experiential learning: Experience as the source of learning and
development, Prentice-Hall, Englewood Cliffs, N. J.
Lofland, J. & Lofland, L. (1995). Analyzing Social Settings; A Guide to Qualitative
Observation and Analysis, Wadsworth, Belmont, California.
Margerison, C. (1988) Action learning and excellence in management development.
Journal of Management Development, 7, 5, 43-53.
Marsick, V. & O’Neil, J. (1999) The many faces of action learning, Management Learning,
30, 2, 159-176.
Maznevski, M. L., & Chudoba, K.M. (2000) Bridging space over time: Global virtual team
dynamics and effectiveness. Organization Science, 11, 5, 473-492.
Nonaka. I., (1994) A dynamic theory of knowledge creation. Organizational Science, 5, 1,
14-37.
Nonaka, I. & Toyama, R. (2003) The knowledge-creating theory revisited: Knowledge
creations as a synthesizing process. Knowledge Management Research and Practice, 1, 210.
Pascoe, C., Ali, I., & Warne, L. (2002) Collaborative knowledge development, dialogue and
team building: Findings from research into social learning in the Australian defense
organization. In Knowledge Management in Context, Burstein, F and Linger, H (eds.)
Australian Scholarly Publishing, Australia.
Pauleen, D. (2003-4) An inductively derived model of leader-initiated relationship building
with virtual team members. Journal of Management Information Systems. 4, 3, 227-256.
Probst, G. (2000) Putting knowledge to work: Case-writing as a knowledge management
and organizational learning tool. In Knowledge Management Case Book (Davenport, T and
Probst, G. (Eds.), Publicis MCD Verlag and John Wiley & Sons, Munich.
Quintas, P., Lefrere, P. & Jones, G. 1997 Knowledge management: A strategic agenda.
Long Range Planning, 30, 385-91.
Revans, R. (1982) The Origins and Growth of Action Learning, Chartwell-Bratt, Bromley.
19
Richards, T. & Richards, L. (1994) Using computers in qualitative research. In Handbook of
Qualitative Research, Lincoln, Y.S. & Denzin, N.K (eds), pp. 445-463. Sage Publications,
Thousand Oaks, CA.
Roth, J. (2003) Enabling knowledge creation: learning from an R & D organization. Journal
of Knowledge Management, 7, 1, 32-48.
Schon, D. (1983) The Reflective Practitioner: How Professionals Think in Action, Basic
Books, New York.
Schon, D. (1987) Educating the Reflective Practitioner: Toward a New Design for Teaching
and Learning in the Professions, Jossey-Bass, San Francisco.
Schwartz, R.M. (1994) The Skilled Facilitator: Practical Wisdom for Developing Effective
Groups. Jossey-Bass Publishers, San Francisco, CA.
Senge, P.M. (1990 ) The Fifth Discipline, Doubleday, New York.
Stern, P. (1987). Grounded theory methodology: Its uses and process. In Nursing Science
Methods, Gortner, S. (ed.), , pp. 79-88. University of California, School of Nursing, San
Francisco.
Strauss, A. L., & Corbin, J (1990) Basics of Qualitative Research: Grounded Theory
Procedures and Techniques, Sage Publications, Newbury Park.
Sveiby, K. (2001) What is knowledge management? accessed September, 2002 at
http://www.sveiby.com/articles/KnowledgeManagement.html
Thomas, J.C., Kellog, W.A., & Erickson, T. (2001) The knowledge management puzzle:
human and social factors in knowledge management. IBM Systems Journal, 40, 4, 863-884.
Townsend, A., DeMarie, S., & Hendrickson, A. (1998) Virtual teams: Technology and the
workplace of the future. Academy of Management Executive, 12, 3, 17-29.
Wenger, E. (1998) Communities of practice: Learning as a social system. The Systems
Thinker, 9, 5, 1–12.
Wiig, K., de Hoog, R., & van der Spek, R. (1997) Knowledge management: A selection of
methods and techniques. Journal of Expert Systems with Applications, 13, 1, 15-27.
Yoong, P. (1996). A Grounded Theory of Reflective Facilitation: Making The Transition
From Traditional To GSS Facilitation. Unpublished doctoral thesis, Victoria University of
Wellington, New Zealand.
Yoong, P. and Pauleen, D. (2004) Generating and analysing data for research on emerging
technologies: A Grounded action learning approach. Information Research Journal, 2004,
9, 4, Available online: http://informationr.net/ir/9-4/paper195.html
Warne, L., Ali, I., & Linger, C. (2002) Representing social learning in the context of
knowledge management: An architectural perspective. Proceedings of AKMMIDS 2002,
20
Melbourne,
9-10
December
02.
Weiss, L.M., Capozzi M.M., & Prusak, P. (2004) Learning from the Internet giants. Sloan
Management Review, 45, 4, 79–84.
Wiig, K. (1997), Knowledge management: an introduction and perspective. The Journal of
Knowledge Management, 1, 1, 6-14.
Wiig, K. (2000) Knowledge management: an emerging discipline rooted in a long history.
In Knowledge Horizons, Dupres, C. & Chauvel, D. (eds), , pp.3-26, ButterworthHeinemann, Boston.
21