The Cynefin Framework: A Tool for Analyzing Qualitative Data in Information Science?
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Citation: McLeod, Julie and Childs, Sue (2013) The Cynefin framework: A tool for analyzing
qualitative data in information science? Library & Information Science Research, 35 (4). pp.
299-309. ISSN 0740-8188
Published by: Elsevier
URL: http://dx.doi.org/10.1016/j.lisr.2013.05.004
<http://dx.doi.org/10.1016/j.lisr.2013.05.004>
This version was downloaded from Northumbria Research Link:
http://nrl.northumbria.ac.uk/12878/
Northumbria University has developed Northumbria Research Link (NRL) to enable users to
access the University’s research output. Copyright © and moral rights for items on NRL are
retained by the individual author(s) and/or other copyright owners. Single copies of full items
can be reproduced, displayed or performed, and given to third parties in any format or
medium for personal research or study, educational, or not-for-profit purposes without prior
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without formal permission of the copyright holder. The full policy is available online:
http://nrl.northumbria.ac.uk/policies.html
This document may differ from the final, published version of the research and has been
made available online in accordance with publisher policies. To read and/or cite from the
published version of the research, please visit the publisher’s website (a subscription may be
required.)
qualitative data in information science? Library & Information Science Research, 35 (4). pp.
299-309. ISSN 0740-8188
Published by: Elsevier
URL: http://dx.doi.org/10.1016/j.lisr.2013.05.004
<http://dx.doi.org/10.1016/j.lisr.2013.05.004>
This version was downloaded from Northumbria Research Link:
http://nrl.northumbria.ac.uk/12878/
Northumbria University has developed Northumbria Research Link (NRL) to enable users to
access the University’s research output. Copyright © and moral rights for items on NRL are
retained by the individual author(s) and/or other copyright owners. Single copies of full items
can be reproduced, displayed or performed, and given to third parties in any format or
medium for personal research or study, educational, or not-for-profit purposes without prior
permission or charge, provided the authors, title and full bibliographic details are given, as
well as a hyperlink and/or URL to the original metadata page. The content must not be
changed in any way. Full items must not be sold commercially in any format or medium
without formal permission of the copyright holder. The full policy is available online:
http://nrl.northumbria.ac.uk/policies.html
This document may differ from the final, published version of the research and has been
made available online in accordance with publisher policies. To read and/or cite from the
published version of the research, please visit the publisher’s website (a subscription may be
required.)
The Cynefin framework: A tool for analyzing qualitative data in information science?
Library & Information Science Research
1. Introduction
One of the challenging steps in qualitative research is interpreting the data collected
and presenting it in ways that enable potential beneficiaries of the research to use it readily
and appropriately. In the information science discipline beneficiaries include both academics
and practitioners with a diverse range of interests. With the heightened attention of
governments and other funding bodies on demonstrating the broader impact of research (e.g.
National Science Foundation, 2013; Research Councils UK, no date), finding appropriate,
tailored ways of presenting or repackaging research results so that they can be used to make a
differences increasingly important. The Cynefin framework presents one way of doing this.
This critical evaluation explores using the Cynefin framework (Snowden & Boone,
2007), which is rooted in knowledge management and complexity science, to interpret a rich
and nuanced set of qualitative data collected from a three-year research project. That project
engaged people worldwide to explore issues and practical strategies for accelerating the pace
of positive change in managing electronic records. While electronic records management
(ERM) is a rather specific information management context, Cynefin has potential as a
research tool in the wider information science discipline.
For organizations, records are “information created, received and maintained as
evidence and as an asset by an organization or person, in pursuance of legal obligations or in
the transaction of business” (International Organization for Standardization [ISO], 2011,
Clause 3.1.7) which need to be managed from creation to disposition. Electronic records,
particularly those that are born digital, are more challenging to manage because they can
comprise a combination of forms (e.g., text, audio, and image), can be distributed across
different systems (e.g., websites and various business systems) and can be dynamic (e.g., an
individual record constructed from different tables in a database).
In the mid-1990s, McDonald (1995) likened the management of electronic records in
the modern unstructured office environment to the wild frontier. Wild because information
technology (IT) was democratizing, decentralizing, individualizing, and personalizing the
way people used and managed information and records in the workplace; and a frontier
because records managers and archivists were questioning concepts and pushing at the
boundaries of knowledge and theory to address the challenge of managing records in the
electronic age.
Corporate rules of the road and other mechanisms have yet to be established in the
electronic world. The wild frontier is unfortunately more the norm than the exception.
In the modern office, it is the office worker, not the technical specialist, who works
with technology applications on a daily basis. It is the office worker, not the
organization, who decides what information will be created, transmitted, and stored.
And it is more often than not the office worker, not the organization, who makes up
the rules, if any. (McDonald, 1995, p.71)
Recognizing the challenge, researchers looked for ways to tame McDonald’s wild
frontier. Seminal research includes that on identifying requirements for managing electronic
records and tactics for satisfying those requirements (Bearman, 1994); on protecting the
integrity of electronic records (Duranti & McNeil, 1996) and maintaining their authenticity
and reliability over time (Duranti & Preston, 2008); and on requirements for recordkeeping
1
Library & Information Science Research
1. Introduction
One of the challenging steps in qualitative research is interpreting the data collected
and presenting it in ways that enable potential beneficiaries of the research to use it readily
and appropriately. In the information science discipline beneficiaries include both academics
and practitioners with a diverse range of interests. With the heightened attention of
governments and other funding bodies on demonstrating the broader impact of research (e.g.
National Science Foundation, 2013; Research Councils UK, no date), finding appropriate,
tailored ways of presenting or repackaging research results so that they can be used to make a
differences increasingly important. The Cynefin framework presents one way of doing this.
This critical evaluation explores using the Cynefin framework (Snowden & Boone,
2007), which is rooted in knowledge management and complexity science, to interpret a rich
and nuanced set of qualitative data collected from a three-year research project. That project
engaged people worldwide to explore issues and practical strategies for accelerating the pace
of positive change in managing electronic records. While electronic records management
(ERM) is a rather specific information management context, Cynefin has potential as a
research tool in the wider information science discipline.
For organizations, records are “information created, received and maintained as
evidence and as an asset by an organization or person, in pursuance of legal obligations or in
the transaction of business” (International Organization for Standardization [ISO], 2011,
Clause 3.1.7) which need to be managed from creation to disposition. Electronic records,
particularly those that are born digital, are more challenging to manage because they can
comprise a combination of forms (e.g., text, audio, and image), can be distributed across
different systems (e.g., websites and various business systems) and can be dynamic (e.g., an
individual record constructed from different tables in a database).
In the mid-1990s, McDonald (1995) likened the management of electronic records in
the modern unstructured office environment to the wild frontier. Wild because information
technology (IT) was democratizing, decentralizing, individualizing, and personalizing the
way people used and managed information and records in the workplace; and a frontier
because records managers and archivists were questioning concepts and pushing at the
boundaries of knowledge and theory to address the challenge of managing records in the
electronic age.
Corporate rules of the road and other mechanisms have yet to be established in the
electronic world. The wild frontier is unfortunately more the norm than the exception.
In the modern office, it is the office worker, not the technical specialist, who works
with technology applications on a daily basis. It is the office worker, not the
organization, who decides what information will be created, transmitted, and stored.
And it is more often than not the office worker, not the organization, who makes up
the rules, if any. (McDonald, 1995, p.71)
Recognizing the challenge, researchers looked for ways to tame McDonald’s wild
frontier. Seminal research includes that on identifying requirements for managing electronic
records and tactics for satisfying those requirements (Bearman, 1994); on protecting the
integrity of electronic records (Duranti & McNeil, 1996) and maintaining their authenticity
and reliability over time (Duranti & Preston, 2008); and on requirements for recordkeeping
1
The Cynefin framework: A tool for analyzing qualitative data in information science?
Library & Information Science Research
metadata (Evans, McKemmish, & Bhoday, 2005; McKemmish, Acland, & Reed, 1999).
Notable projects include those of the National Archives of Canada (1991) and Indiana
University (2002). Research has been complemented by the development of many guidelines
and standards (e.g., ARMA International, 2009; DLM Forum, 2001, 2008, 2010;
International Council on Archives [ICA], 2008; ISO, 2001, 2011; State Records, New South
Wales, 2003, 2007; Department of Defense, 1997, 2007), and many commercial electronic
document and records management (RM) systems. However, despite these significant
developments, the management of electronic records continues to be a challenge for
organizations, as evidenced by widely reported security breaches (e.g., unsecured health
records reported to the US Department of Health & Human Services) and failures of large
scale IT systems (e.g. the scrapping of the National Health Service [NHS] national IT
program in the UK).
Reflecting a decade after his wild frontier article, McDonald (2005) felt that, though
some progress had been made, the wild frontier had not yet been tamed. The pace of change
had been relatively slow because organizations do not understand how the office of today
functions, nor how it could benefit from advanced tools for managing work processes and
their associated records. A key inhibitor to progress was managers’ lack of understanding
about records and RM; further, to make progress required a “focus on establishing a vision,
enhancing awareness, assigning accountability, designing an architecture and building
capacity” (McDonald, 2005, p. 8). McDonald’s views influenced the authors of this article to
conduct a research project (AC + erm1) that explored issues and practical strategies with the
aim of helping accelerate improvements in ERM. It provides the data for the study reported
here.
The case of ERM appeared to be complex and the AC + erm project findings needed to
be better understood. The Cynefin framework was then selected to achieve that. However,
ERM is not untypical of the types of systems challenges that information science research
explores; and the rich, nuanced data gathered is typical of that obtained in other qualitative
information science projects.
2. Problem statement
Making sense of research data in a way that enables it to be more readily usable by
practitioners and other stakeholders is one important pathway to ensuring the research
findings can be translated into practice so that the research has impact. A wealth of
qualitative data was obtained by the AC + erm project, covering the experiences and expertise
of a wide range of participants - academics, practitioners, RM leaders. Rich and nuanced, the
dataset comprises an extensive range of ERM issues and problems with associated solutions
that, in the participants’ experience, had worked or not worked(McLeod, Childs, &
Hardiman, 2010).As the challenge of ERM affects all organizations, the potential
beneficiaries of this research are many and diverse. The task was how to enable them to use
the AC + erm results and to adopt or adapt the solutions to improve the management of their e-
records. Though the data were analyzed and presented in a wide variety of forms (e.g. textual
categorized themes, tables of ranked numeric data, phenomenological reflective prose, mind
maps, word clouds, rich pictures, narratives, games) freely available from the project website,
1
AC+erm (Accelerating the pace of positive Change in Electronic Records Management) is pronounced āsirm;
the + is silent, indicating only that change is positive. http://www.northumbria.ac.uk/acerm
2
Library & Information Science Research
metadata (Evans, McKemmish, & Bhoday, 2005; McKemmish, Acland, & Reed, 1999).
Notable projects include those of the National Archives of Canada (1991) and Indiana
University (2002). Research has been complemented by the development of many guidelines
and standards (e.g., ARMA International, 2009; DLM Forum, 2001, 2008, 2010;
International Council on Archives [ICA], 2008; ISO, 2001, 2011; State Records, New South
Wales, 2003, 2007; Department of Defense, 1997, 2007), and many commercial electronic
document and records management (RM) systems. However, despite these significant
developments, the management of electronic records continues to be a challenge for
organizations, as evidenced by widely reported security breaches (e.g., unsecured health
records reported to the US Department of Health & Human Services) and failures of large
scale IT systems (e.g. the scrapping of the National Health Service [NHS] national IT
program in the UK).
Reflecting a decade after his wild frontier article, McDonald (2005) felt that, though
some progress had been made, the wild frontier had not yet been tamed. The pace of change
had been relatively slow because organizations do not understand how the office of today
functions, nor how it could benefit from advanced tools for managing work processes and
their associated records. A key inhibitor to progress was managers’ lack of understanding
about records and RM; further, to make progress required a “focus on establishing a vision,
enhancing awareness, assigning accountability, designing an architecture and building
capacity” (McDonald, 2005, p. 8). McDonald’s views influenced the authors of this article to
conduct a research project (AC + erm1) that explored issues and practical strategies with the
aim of helping accelerate improvements in ERM. It provides the data for the study reported
here.
The case of ERM appeared to be complex and the AC + erm project findings needed to
be better understood. The Cynefin framework was then selected to achieve that. However,
ERM is not untypical of the types of systems challenges that information science research
explores; and the rich, nuanced data gathered is typical of that obtained in other qualitative
information science projects.
2. Problem statement
Making sense of research data in a way that enables it to be more readily usable by
practitioners and other stakeholders is one important pathway to ensuring the research
findings can be translated into practice so that the research has impact. A wealth of
qualitative data was obtained by the AC + erm project, covering the experiences and expertise
of a wide range of participants - academics, practitioners, RM leaders. Rich and nuanced, the
dataset comprises an extensive range of ERM issues and problems with associated solutions
that, in the participants’ experience, had worked or not worked(McLeod, Childs, &
Hardiman, 2010).As the challenge of ERM affects all organizations, the potential
beneficiaries of this research are many and diverse. The task was how to enable them to use
the AC + erm results and to adopt or adapt the solutions to improve the management of their e-
records. Though the data were analyzed and presented in a wide variety of forms (e.g. textual
categorized themes, tables of ranked numeric data, phenomenological reflective prose, mind
maps, word clouds, rich pictures, narratives, games) freely available from the project website,
1
AC+erm (Accelerating the pace of positive Change in Electronic Records Management) is pronounced āsirm;
the + is silent, indicating only that change is positive. http://www.northumbria.ac.uk/acerm
2
The Cynefin framework: A tool for analyzing qualitative data in information science?
Library & Information Science Research
anecdotal evidence in feedback and comments from users indicated they were unsure about
how to apply the findings in their own contexts. The presentations were too detailed, too
granular, and used research terminology. A different way of interpreting the analyzed data
and presenting it in a form (or forms) that would enable the findings to be more readily used
by practitioners in their own contexts was needed.
The Cynefin framework (Snowden & Boone, 2007) was selected to undertake a
secondary analysis, based on the nature of the ERM challenge and the research data. Cynefin
has not been widely used as a research data analysis technique or in the information science
discipline. The study presented here addresses the key question:
Can the Cynefin framework be used to further analyze and interpret the research data
and better understand what solution(s) might be most/least appropriate for a particular
ERM issue, given the conclusion from the AC + erm project that tactics and solutions
for ERM are contextualised, contingent and complex?
3. Literature review
The Cynefin framework was developed from research conducted over a period of
years by Snowden and colleagues (Snowden, 2010). It is a framework which helps decision
makers to make sense of a range of business problems and situations, in different dynamic
contexts, and to take appropriate action. Because of this, it appeared to offer an appropriate
approach for making sense of the AC + erm data and linking the issues to solutions to support
appropriate action for change.
The conceptual thinking that underpins the framework draws from knowledge
management and complexity science. Cynefin comprises five domains (Figure 1)
representing the types of situations or environments that organizations typically experience
and need to respond to and manage (Lambe, 2007, p. 134). The domains are predicated on the
construct of order (Snowden, 2005, 2010). The ordered domains are labelled simple and
complicated; the un-ordered ones complex and chaos; and the fifth domain, the central area,
is the domain of disorder. It is important to appreciate that un-order is not lack of order (i.e.,
its opposite), but a different type of order; order that is not directed or designed, but
“emergent” (Kurtz and Snowden, 2003). The characteristics of the domains are summarized
in Table 1 and explained below, based on Snowden and colleagues’ many publications (in
particular, Kurtz & Snowden, 2003; Snowden, 2002, 2003, 2005, 2010; Snowden & Boone,
2007).
Insert Fig. 1. Cynefin framework from Snowden (2010, Pt.7)
Insert Table 1. Summary explanation of the four Cynefin domains: simple, complicated,
complex, chaos
The simple domain is characterized by clear cause and effect. The decision model is
to sense the situation, categorize it, and respond based on best practice. The domain of
efficiency, there is often a right answer; standard operating procedures and process re-
engineering are appropriate practices. Mortgage payment processing would fall in the simple
domain. The complicated domain is also characterized by cause and effect but there may be
multiple right answers. The decision model is therefore to sense, analyze, and respond. This
requires expertise to choose the appropriate answer (i.e., good, rather than best, practice).
Possible practices are systems thinking and scenario planning. Designing a new repository for
research outputs/data falls in this domain.
3
Library & Information Science Research
anecdotal evidence in feedback and comments from users indicated they were unsure about
how to apply the findings in their own contexts. The presentations were too detailed, too
granular, and used research terminology. A different way of interpreting the analyzed data
and presenting it in a form (or forms) that would enable the findings to be more readily used
by practitioners in their own contexts was needed.
The Cynefin framework (Snowden & Boone, 2007) was selected to undertake a
secondary analysis, based on the nature of the ERM challenge and the research data. Cynefin
has not been widely used as a research data analysis technique or in the information science
discipline. The study presented here addresses the key question:
Can the Cynefin framework be used to further analyze and interpret the research data
and better understand what solution(s) might be most/least appropriate for a particular
ERM issue, given the conclusion from the AC + erm project that tactics and solutions
for ERM are contextualised, contingent and complex?
3. Literature review
The Cynefin framework was developed from research conducted over a period of
years by Snowden and colleagues (Snowden, 2010). It is a framework which helps decision
makers to make sense of a range of business problems and situations, in different dynamic
contexts, and to take appropriate action. Because of this, it appeared to offer an appropriate
approach for making sense of the AC + erm data and linking the issues to solutions to support
appropriate action for change.
The conceptual thinking that underpins the framework draws from knowledge
management and complexity science. Cynefin comprises five domains (Figure 1)
representing the types of situations or environments that organizations typically experience
and need to respond to and manage (Lambe, 2007, p. 134). The domains are predicated on the
construct of order (Snowden, 2005, 2010). The ordered domains are labelled simple and
complicated; the un-ordered ones complex and chaos; and the fifth domain, the central area,
is the domain of disorder. It is important to appreciate that un-order is not lack of order (i.e.,
its opposite), but a different type of order; order that is not directed or designed, but
“emergent” (Kurtz and Snowden, 2003). The characteristics of the domains are summarized
in Table 1 and explained below, based on Snowden and colleagues’ many publications (in
particular, Kurtz & Snowden, 2003; Snowden, 2002, 2003, 2005, 2010; Snowden & Boone,
2007).
Insert Fig. 1. Cynefin framework from Snowden (2010, Pt.7)
Insert Table 1. Summary explanation of the four Cynefin domains: simple, complicated,
complex, chaos
The simple domain is characterized by clear cause and effect. The decision model is
to sense the situation, categorize it, and respond based on best practice. The domain of
efficiency, there is often a right answer; standard operating procedures and process re-
engineering are appropriate practices. Mortgage payment processing would fall in the simple
domain. The complicated domain is also characterized by cause and effect but there may be
multiple right answers. The decision model is therefore to sense, analyze, and respond. This
requires expertise to choose the appropriate answer (i.e., good, rather than best, practice).
Possible practices are systems thinking and scenario planning. Designing a new repository for
research outputs/data falls in this domain.
3
The Cynefin framework: A tool for analyzing qualitative data in information science?
Library & Information Science Research
Unpredictability and flux characterize the complex domain. Cause and effect are only
understood in retrospect; experimentation is required to find answers. The decision model is
therefore to probe first, then sense and respond; practice emerges. The early strategic
adoption of cloud computing in organizations falls into this domain in the absence of
established best or good practice for implementation. As emergent practice becomes good
practice this example would move to complicated and, ultimately, simple domains.
Turbulence and lack of any link between cause and effect characterize the domain of chaos.
In the absence of any right answers the decision model must be to act first and then sense and
respond, (i.e., crisis management). This can lead to innovative practice; for example, the US
response to the 2010 Haiti Earthquake, where social media technologies were used for the
first time in a crisis as the main knowledge sharing mechanisms (Yates & Paquette, 2011).
The fifth domain, disorder, is where people are unable to decide which of the other
domains represents their situation. This domain can be reduced in size through discussion to
reach consensus about the nature of the situation and the most appropriate type of response,
(i.e., moving to another domain or domains).
The tetrahedrons in Figure 1 are a vital part of Cynefin. They represent the
connections between the center (e.g. managers) and the constituents (e.g. staff). In the
ordered domains (simple and complicated) connections between a central manager and staff
are strong. In the unordered domains (complex and chaos) they are weak. Differences in the
connections represent different work patterns: co-ordination (simple), co-operation
(complicated), collaboration (complex) and directive intervention (chaos).
No domain is more desirable than another: They just describe the situation facing the
organization.
Snowden (2010, Pt.1) notes that the early versions of the Cynefin framework were
based on ideas from knowledge management; for example, the Information Space (I-Space)
model for understanding information flows (Boisot & Cox, 1999); the SECI model
(socialisation, externalisation, combination, internalisation) where the interaction between
tacit and explicit knowledge produces a spiral of knowledge creation (Nonaka & Takeuchi,
1995); and organizational learning cultures (Senge, 2006). As the Cynefin framework
developed, Snowden (2010, Pt.2) brought in the aspect of decision-making, drawing on ideas
from complexity science, particularly the concept of complex adaptive systems (CAS).
Complexity science (Burnes, 2005; Stacey, 2011) was developed by researchers in disciplines
working with natural systems and briefly comprises three key concepts: (a) chaos theory -
some dynamic systems are non-linear, demonstrating complex patterns that are not directly
proportional to, nor predicted from, their causes/inputs; (b) dissipative structure theory - some
systems can pass through states of instability/randomness to new organized states by self-
organization; and (c) CAS - a system comprising a large number of entities interacting with
each other, following local principles and rules, from which emerges a self-organizing group-
wide pattern not determined by the entities, the emergent patterns, or anything outside the
system. The ideas of complexity theory have been used by many authors to study
organizations, based on the argument that organizations are complex, non-linear, self-
organizing systems. The Cynefin framework incorporates the “metaphorticians” approach
(Richardson, 2008) to the application of complexity theory to organizations, using these ideas
as a different lens to view sense-making and decision-making. A number of authors have
criticised the way that complexity thinking has been applied to organizations (Burnes, 2005;
Mingers & White, 2010; Stacey, 2011; Zhu, 2007). Stacey (2011) proposes a more innovative
adaptation of the CAS idea - a complex responsive processes perspective. He recommends
4
Library & Information Science Research
Unpredictability and flux characterize the complex domain. Cause and effect are only
understood in retrospect; experimentation is required to find answers. The decision model is
therefore to probe first, then sense and respond; practice emerges. The early strategic
adoption of cloud computing in organizations falls into this domain in the absence of
established best or good practice for implementation. As emergent practice becomes good
practice this example would move to complicated and, ultimately, simple domains.
Turbulence and lack of any link between cause and effect characterize the domain of chaos.
In the absence of any right answers the decision model must be to act first and then sense and
respond, (i.e., crisis management). This can lead to innovative practice; for example, the US
response to the 2010 Haiti Earthquake, where social media technologies were used for the
first time in a crisis as the main knowledge sharing mechanisms (Yates & Paquette, 2011).
The fifth domain, disorder, is where people are unable to decide which of the other
domains represents their situation. This domain can be reduced in size through discussion to
reach consensus about the nature of the situation and the most appropriate type of response,
(i.e., moving to another domain or domains).
The tetrahedrons in Figure 1 are a vital part of Cynefin. They represent the
connections between the center (e.g. managers) and the constituents (e.g. staff). In the
ordered domains (simple and complicated) connections between a central manager and staff
are strong. In the unordered domains (complex and chaos) they are weak. Differences in the
connections represent different work patterns: co-ordination (simple), co-operation
(complicated), collaboration (complex) and directive intervention (chaos).
No domain is more desirable than another: They just describe the situation facing the
organization.
Snowden (2010, Pt.1) notes that the early versions of the Cynefin framework were
based on ideas from knowledge management; for example, the Information Space (I-Space)
model for understanding information flows (Boisot & Cox, 1999); the SECI model
(socialisation, externalisation, combination, internalisation) where the interaction between
tacit and explicit knowledge produces a spiral of knowledge creation (Nonaka & Takeuchi,
1995); and organizational learning cultures (Senge, 2006). As the Cynefin framework
developed, Snowden (2010, Pt.2) brought in the aspect of decision-making, drawing on ideas
from complexity science, particularly the concept of complex adaptive systems (CAS).
Complexity science (Burnes, 2005; Stacey, 2011) was developed by researchers in disciplines
working with natural systems and briefly comprises three key concepts: (a) chaos theory -
some dynamic systems are non-linear, demonstrating complex patterns that are not directly
proportional to, nor predicted from, their causes/inputs; (b) dissipative structure theory - some
systems can pass through states of instability/randomness to new organized states by self-
organization; and (c) CAS - a system comprising a large number of entities interacting with
each other, following local principles and rules, from which emerges a self-organizing group-
wide pattern not determined by the entities, the emergent patterns, or anything outside the
system. The ideas of complexity theory have been used by many authors to study
organizations, based on the argument that organizations are complex, non-linear, self-
organizing systems. The Cynefin framework incorporates the “metaphorticians” approach
(Richardson, 2008) to the application of complexity theory to organizations, using these ideas
as a different lens to view sense-making and decision-making. A number of authors have
criticised the way that complexity thinking has been applied to organizations (Burnes, 2005;
Mingers & White, 2010; Stacey, 2011; Zhu, 2007). Stacey (2011) proposes a more innovative
adaptation of the CAS idea - a complex responsive processes perspective. He recommends
4
The Cynefin framework: A tool for analyzing qualitative data in information science?
Library & Information Science Research
that managers should use existing tools and techniques, such as the Cynefin framework, but
in a reflexive way, exercising practical judgment, and accepting that they cannot fully control
how these tools and techniques will work out in a specific, real-world situation (Stacey,
2012).
Cynefin can be used in different organizational contexts and for different purposes;
for example, to gain new insights on a challenging problem or contentious issue, to plan
actions to move a situation from one domain to another, or to consider strategies for
managing different situations (Kurtz & Snowden, 2003).In management science it has been
used primarily in the context of decision-making and leadership (Benson & Dresdow, 2009;
Gonnering, 2010; Moerschell & Lao, 2012; Snowden & Boone, 2007). In health it has been
used in a variety of contexts; for example, choosing approaches to health promotion (Van
Beurden, Kia, Zask, Dietrich,& Rose, 2013), developing research in organizational behavior
(Mark, 2006), analyzing chronic care (Martin et al, 2011), and understanding knowledge in
clinical practice (Sturmberg & Martin, 2008).
There are only a few published uses of Cynefin in information science research. These
relate to information system design and information architecture (Burford, 2011; Lambe,
2007; Snowden, 2001); types of learning supported by an Internet portal (Cronje & Burger
(2006); understanding what enhances productiveness in knowledge generation in science
(Van der Walt & de Wet, 2008); and evaluating library services (Hart & Schenk, 2010).None
of these examples uses the Cynefin workshop technique to analyze empirical qualitative data
in the way this study does.
4. Procedures
4.1. The data
The study uses data from the AC + erm project, a major research project (2007-10) that
explored the design of an organizational-centred architecture for managing electronic records.
AC + erm considered three facets: people, processes, and technology. The issues and problems
of ERM were investigated and examples of ERM strategies, tactics, and practice were
gathered, analyzed and shared. An aim was to produce practical strategies for the
contemporary work environment that were scenario-based rather than organizational-based;
presented issues as well as solutions; were capable of being used in practice, as well as
facilitating discussion and debate; and would produce change.
Recordkeeping in the e-environment involves four stakeholder groups:
executives/senior managers, records professionals, IT/systems administrators and
recordkeepers (ISO 2001, 2011). It also transcends disciplines and sectors. The project
therefore adopted a trans-disciplinary approach and obtained expert opinion from all four
stakeholder groups.
The project’s qualitative methodology comprised three phases: (a) a comprehensive
systematic review of relevant literature published from 1997-2009 to identify ERM issues
(Centre for Reviews and Dissemination, 2008); (b) an investigation of the three facets; and
(c) a major dissemination activity running throughout its life. The investigation phase used a
combination of electronic Delphi studies (gathering expert opinion on a global basis) and
face-to-face colloquia (enabling in-depth discussion on a local (UK) basis). The Delphi
5
Library & Information Science Research
that managers should use existing tools and techniques, such as the Cynefin framework, but
in a reflexive way, exercising practical judgment, and accepting that they cannot fully control
how these tools and techniques will work out in a specific, real-world situation (Stacey,
2012).
Cynefin can be used in different organizational contexts and for different purposes;
for example, to gain new insights on a challenging problem or contentious issue, to plan
actions to move a situation from one domain to another, or to consider strategies for
managing different situations (Kurtz & Snowden, 2003).In management science it has been
used primarily in the context of decision-making and leadership (Benson & Dresdow, 2009;
Gonnering, 2010; Moerschell & Lao, 2012; Snowden & Boone, 2007). In health it has been
used in a variety of contexts; for example, choosing approaches to health promotion (Van
Beurden, Kia, Zask, Dietrich,& Rose, 2013), developing research in organizational behavior
(Mark, 2006), analyzing chronic care (Martin et al, 2011), and understanding knowledge in
clinical practice (Sturmberg & Martin, 2008).
There are only a few published uses of Cynefin in information science research. These
relate to information system design and information architecture (Burford, 2011; Lambe,
2007; Snowden, 2001); types of learning supported by an Internet portal (Cronje & Burger
(2006); understanding what enhances productiveness in knowledge generation in science
(Van der Walt & de Wet, 2008); and evaluating library services (Hart & Schenk, 2010).None
of these examples uses the Cynefin workshop technique to analyze empirical qualitative data
in the way this study does.
4. Procedures
4.1. The data
The study uses data from the AC + erm project, a major research project (2007-10) that
explored the design of an organizational-centred architecture for managing electronic records.
AC + erm considered three facets: people, processes, and technology. The issues and problems
of ERM were investigated and examples of ERM strategies, tactics, and practice were
gathered, analyzed and shared. An aim was to produce practical strategies for the
contemporary work environment that were scenario-based rather than organizational-based;
presented issues as well as solutions; were capable of being used in practice, as well as
facilitating discussion and debate; and would produce change.
Recordkeeping in the e-environment involves four stakeholder groups:
executives/senior managers, records professionals, IT/systems administrators and
recordkeepers (ISO 2001, 2011). It also transcends disciplines and sectors. The project
therefore adopted a trans-disciplinary approach and obtained expert opinion from all four
stakeholder groups.
The project’s qualitative methodology comprised three phases: (a) a comprehensive
systematic review of relevant literature published from 1997-2009 to identify ERM issues
(Centre for Reviews and Dissemination, 2008); (b) an investigation of the three facets; and
(c) a major dissemination activity running throughout its life. The investigation phase used a
combination of electronic Delphi studies (gathering expert opinion on a global basis) and
face-to-face colloquia (enabling in-depth discussion on a local (UK) basis). The Delphi
5
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