Management Learning
Copyright © 2007 Sage Publications
London, Los Angeles, New Delhi
and Singapore
http://mlq.sagepub.com
Vol. 38(3): 277–295
1350–5076
Article
Elena Antonacopoulou
University of Liverpool Management School, UK
Ricardo Chiva
Universitat Jaume I, Spain
The Social Complexity of Organizational
Learning:
The Dynamics of Learning and Organizing
Abstract This article examines the social complexity of Organizational Learning. We built
on and seek to extend recent conceptualizations of Organizational Learning that emphasize
the emergent and fluid nature of learning in organizations, by drawing on some of the principles of Complexity Science. We selectively introduce two sets of principles of complexity that
provide further richness to our understanding of Organizational Learning as a social complex process. The two sets of principles are ‘schemas–diversity’ and ‘interaction–interdependence’. We discuss the main characteristics of these principles of Complexity Science and
show they can help us understand aspects of the social complexity of Organizational
Learning. Our analysis shows that one of the main contributions of the Complexity Science
perspective to understanding Organizational Learning is that it reveals more clearly the
tensions that underpin learning in social contexts. We provide a re-conceptualization of
tensions as revealing elasticity and not only conflict. We argue that Organizational
Learning as a source of tensions keeps the organization in tension, which allows us to better capture the dynamics of learning and organizing. We conclude by outlining some issues
that future OL research would address if OL is conceptualized as a dynamic complex process. Key Words: social complexity; organizational learning; dynamics; organizing; tension
Introduction
The concept of Organizational Learning (hereafter OL) has experienced an important growth over recent years attracting the attention of both academics and practitioners. In spite of its popularity and consolidation, the OL debate has not progressed
much beyond conceptualizations of learning as an integral part of organizing. Several
recent contributions (Vince et al., 2002; Lyles and Easterby-Smith, 2003; Easterby-Smith
DOI: 10.1177/1350507607079029
278
Management Learning 38(3)
et al., 2004) highlight a number of unresolved issues and point to new directions for
future research in the field. Some particularly important areas in need of addressing
are the social and political forces that define OL and shape the learning practices and
the way these emerge in the process of human action and interaction.
We seek to respond to these calls in this article by focusing on the dynamics of
learning and organizing. The dynamic nature of learning and organizing draw attention to the social complexity of OL. By social complexity we do not simply seek to reinforce the social character of learning in organizations. This has been widely debated
in the OL literature and has rightfully drawn attention to the situated, institutionalized
and negotiated nature of learning in relation to organizing (Gherardi et al., 1998;
Elkjaer, 2003). By social complexity we want to extend the current conceptualizations
of OL by drawing more attention to the role of tensions in relation to learning that
define its emergent nature. Tensions do not only reveal the dynamic nature of learning, they also provide further explanations as to why learning is a political process and
fundamentally has the potential to keep an organization in-tension—agile, flexible
and able to change in the context of an uncertain environment.
To capture the dynamism of OL we seek to extend the scientific base from which
we have hitherto drawn ideas to advance the debate in this field. We do not only rely
on conceptualizations of learning in philosophy, psychology, sociology, education,
anthropology and biology. These scientific lenses have served us well so far in our
research inquiry into OL. In this article, we draw on the ideas of Complexity Science
to develop our re-conceptualization of OL.
During the last decade, several researchers and practitioners have started to use
complexity science to better understand organizational and managerial issues such
as: strategic management (Stacey, 1993, 2003), strategic change (Stacey, 1995; Brown
and Eisenhardt, 1997), innovation management (Cheng and Van de Ven, 1996) and
design management (Chiva, 2004). One of the resulting conceptualizations emanating from the use of Complexity Science is the view of organizations as Complex
Adaptive Systems (hereafter, CAS) (Gell-Mann, 1994; Stacey, 1996; Anderson, 1999;
Axelrod and Cohen, 1999); with a capacity to learn as one of their most important
characteristics (Gell-Mann, 1994; Stacey, 1995, 1996; Sherman and Schultz, 1998).
A basic assumption within these theories is that organizations are composed of semiautonomous agents that seek to maximize fitness by adjusting interpretative and
action-oriented schema that determine how they view and interact with other agents
and the environment (Dooley et al., 2003).
Mathews et al. (1999) argue that the emerging Complexity Science has the potential to extend and enhance our knowledge of organizational change and transformation processes. This view is echoed by Burnes (2005) as well as, by Miner and Mezias
(1996) who also point out the need to link OL with other convergent theoretical
models, such as the science of complexity. Cohen and Sproull (1996) support this
view and recognize that the literature on OL shows a certain affinity with the developments in the literature on CAS. Chiva (2003) takes this view a step further and illustrates that CAS ideas are relevant in identifying the essential factors that facilitate OL.
More recently, Luoma (2006) applies principles of complexity science in rethinking
management development, while Antonacopoulou (2006a) employs complexity principles to show the complex nature of learning in relation to working and living.
This article seeks to make a contribution to this debate by providing further
insights into the ways in which a CAS perspective can contribute to the study of OL.
Antonacopoulou & Chiva: The Social Complexity of Organizational Learning
279
The main focus of this article is to identify new ways we can understand and represent better the social complexity which is so central to OL. We will argue that social
complexity brings us closer to the micro-foundations of OL and allows an engagement with the dynamics of learning and organizing. The dynamics of learning and
organizing give voice not only to power and political forces influencing learning.
Dynamics reveal the tensions that underpin the emerging social order as well as, the
fluidity of social interactions.
We organize the discussion in three main parts. We begin with a brief overview of
the main principles of CAS. We draw particular attention to the principles of
Complexity Science that can usefully speak to social complexity. We recognize the
challenges Complexity Science as a new field presents and take care in selecting the
principles of complexity that we focus on. We focus on two sets of principles of
Complexity Science—‘Schemas–Diversity’ and ‘Interaction–Interdependence’ that
are relevant to understanding social complexity and which other researchers have
also employed to study organizational phenomena (see Burnes, 2005; SammutBonnici and Wensley, 2002; Stacey et al., 2002). We apply these principles to our
analysis and re-conceptualization of OL as a dynamic, complex process. Based on this
conceptualization, in the second section, we seek to integrate the main perspectives
in the OL debate and to propose an integrated view—a dynamic view—of OL. We
discuss the main dimensions of the dynamic view of OL and identify Engagement,
Multiplicity and Inter-connectivity as key dimensions of the social complexity of OL.
In the third section, we discuss the implications of the social complexity of OL for
future OL research. We introduce a range of issues that could form part of the future
OL research agenda. These issues draw attention to the methodological challenges
of capturing the dynamics which shape how organizing and learning emerge and
co-evolve as constitutive of each other.
Social Complexity and Complexity Science
Complexity Science emerged in the 1960s, although its true upsurge was not seen
until the mid-1980s. Complexity Science sets out to devise mechanisms to create and
maintain complexity, and to produce tools for its description and analysis (Simon,
1996). Complexity Science covers many fields of scientific research (such as chaos
theory and the study of fractals) and has its roots in the physical sciences (GellMann, 1994; Coleman, 1999; Axelrod and Cohen, 1999), a point, which understandably can be controversial particularly given our effort in this article to apply
some principles of Complexity Science to understand social complexity.
The study of CAS is integral to Complexity Science because as Sherman and Schultz
(1998: 17) explain, CAS are: ‘composed of interacting “agents” following rules,
exchanging influence with their local and global environments and altering the very
environment they are responding to by virtue of their simple actions’. According to
Stacey (2003), the main difference between CAS ideas and other theories of complexity like chaos or dissipative structures is that while the latter seek to construct models of systems at the macro level, CAS ideas focus more on a micro level and seek to
model agent’s rules and their interaction to explain their behaviour (Burnes, 2005).
The CAS view has been the main perspective that has so far informed organization studies using the principles of Complexity Science yet, noticeably as some
280
Management Learning 38(3)
scholars emphasize, the way Complexity Science and CAS more specifically have
been employed and interpreted has varied. According to Griffin et al. (1998: 330)
and Stacey (2003: 252), there are two perspectives on the nature of CAS. The orthodox perspective, typified by the views of Holland (1995) and Gell-Mann (1994)
presents complex systems in somewhat mechanistic, reductionist terms and is modelled by an objective observer in the interest of predicting its behaviour. Self-organization is not seen to be a new ordering principle in the evolution of the system.
Evolution occurs through random mutation and competitive selection. The system
is modelled as a network of cybernetic and cognitivistic agents that represent regularities in the form of rules (schemas). Houchin and MacLean (2005) name this
perspective the ‘rules-based approach’ as rules play an essential role in the emergence of a new order.
In contrast, the radical perspective is typified by the views of Kauffman (1995) and
Goodwin (1994) who emphasize self-organization, rather than random mutation, as
central to the emergence of new forms. Goodwin (1994) stresses the importance of
participatory engagement for understanding the processes of emergence. Houchin
and MacLean (2005) label this perspective the ‘connectionist approach’ since connections, interactions and interdependence play an essential role in the emergence
of new forms. This perspective therefore, suggests that emergence and self-organization are governed by an immanent rationale that goes beyond the correlation of
causes and effects and that a generative process is at work (Griffin et al., 1998).
Some organization studies literature (Morgan, 1997; Brown and Eisenhardt,
1997) on complexity assumes that self-organization can be controlled. These perspectives seem to take ideas from the radical perspective, although they are clearly
orthodox in their approach to complexity. Tsoukas (1998) provides some explanation to this observation by pointing out the two approaches that tend to be employed
when scholars seek to engage with Complexity Science to understand organizational and management issues. The first approach is typical of scholars who draw analogies between organizations and organisms (Gregersen and Sailer, 1993; Stacey, 1995;
Thietart and Forgues, 1995). The second approach is typical of scholars who have
strong doubts about its applicability, because human systems are not like other systems in the physical world (Johnson and Burton, 1994).
In this article, our analysis will focus on the radical perspective of CAS, in terms
of considering self-organization and emergence to be key aspects in the evolution of
organizational forms. We will not reject concepts like schemas or rules as these refer
to micro-dynamics in relation to knowing and doing that are central to organizing.
Equally, we will draw on the principles of CAS not as analogies but as dimensions
that are relevant to social systems such as organizations and can help us explain
social complexity. The complexity principles we will focus on are: Schemas–Diversity
and Interaction–Interdependence.
We choose these two sets of principles both because they reflect the most significant dimensions that explain the nature of CAS and also because they correspond to
the two main perspectives in OL research—the acquisition metaphor and the participation metaphor (see Elkjaer, 2004). A linchpin of the acquisition metaphor is schema
theory (Gnyawali and Stewart, 2003), which considers learning as the process of
transforming, creating, refining or validating schemas. On the other hand, the participation metaphor considers learning as the product of social interactions (Gherardi
et al., 1998).
Antonacopoulou & Chiva: The Social Complexity of Organizational Learning
281
Our objective is to use Complexity Science as a lens that can help open up the
debate in OL to reveal other potential conceptualizations of the phenomenon. Our
aim is to encourage OL researchers to imagine OL as an emergent, dynamic, complex process where tensions affect learning and organizing. We are clearly not the
first to suggest that learning and organizing are emergent processes (see Clegg et al.,
2005), nor are we the first to point to the socio-political dynamics of learning (see
Lawrence et al., 2005). Our contribution is in illustrating how an application of
Complexity principles can help extend our current research questions in OL
research revealing how dynamic learning and organizing can contribute to organizational agility, ambidexterity and flexibility.
Schemas–Diversity
Social systems have underlying structures that enable them to co-ordinate the multiplicity of agents they bring together. The notion of schema more aptly describes
these structures as their key characteristic is flexibility rather than rigidity which may
often be associated with structures. Schemas are created by actors in an interactive
relationship and provide a framework enabling agents to anticipate the results of
their actions (Holland, 1995; Anderson, 1999; Stacey, 1996). In Complexity Science
schemas are not limited to cognitive orientations but also encompass ‘models’,
‘rules’, ‘routines’, ‘tendencies’ (Axelrod and Cohen, 1999; Stacey et al., 2002;
Sammut-Bonnici and Wensley, 2002). A schema can be defined ‘as a set of rules that
reflects regularities in experience and enables a system to determine the nature of
further experience and make sense of it’ (Stacey, 1996: 289). According to Burnes
(2005: 80), one of the most significant findings to come out of complexity theory is
that the emergence of order manifests itself through a process of self-organization
that occurs through the operation of a limited number of simple order generating
rules or schemas.
Agents scan their environment and develop schemas, which represent interpretative and action rules. Schemas shape agents’ actions and vice versa. The interaction
that governs the relationship between schemas and agents is not simply a mutually
reinforcing co-existence. A number of conditions shape how schemas are translated
into action and how behaviours emerge from rules. A set of rules reflects the habitual experiences of agents that also shape how future experiences are defined and
engaged with. According to Stacey (1996), the rules are coded in the form of symbols
such as mental images, numbers, colours, shapes and so on. In the case of a firm, this
would depend on the nature of the schemas whether they are concerned with financial policy, strategic management or product design.
We would argue that social systems more broadly would reflect schemas in routines
and practices. This point finds support in Axelrod and Cohen’s (1999) contention
that schemas can be related to organizational routines, as these are recurring patterns of interaction among agents and artefacts. Routines arise because interactions
increase the likelihood of later repetitions of the same interactions. This means that
routines are not standard operating procedures but are constantly co-evolving as
social structures, agents and artefacts create the conditions that govern their interaction. This view is consistent with the recent analysis by Feldman (2004) and Pentland
and Feldman (2005) on routines.
282
Management Learning 38(3)
In general, schema-change has the effect of making agents more robust (they can
perform in the light of increasing variation or variety), more reliable (they can perform more predictably), or grow in requisite variety (they can adapt to a wider range
of conditions). Therefore, schema-change implies learning in social systems. In turn
it can also be argued that learning is not only a social process underlying the way
agents, their structure and artefacts interact. It is also a dynamic force shaping the
ways in which emerging rules are consolidated, updated, reviewed and constantly
changed through agents’ actions and interactions with one another. This point
suggests that social complexity reveals a generative dance between serendipity and
multiplicity. The latter point provides further explanations about the process of
emergence due to diversity (Luoma, 2006).
In social systems, each agent is different from the others, and its performance
depends on the other agents and the system itself, which influences its behaviour.
The context therefore, takes on a vitally important role. It is defined by the agents
and defines the agents’ actions. This interdependence also helps clarify why each
agent carries out a function within a particular context which itself is defined by the
agents’ relationships with other agents (Holland, 1995). It is this diversity that allows
a social system to remain viable. The inherent heterogeneity provides the basis for
renewing the system and the social conditions that allow it to function. Diversity
underpins the social dynamics that feed the complex social interactions.
Interaction–Interdependence
Social systems are made up of heterogeneous agents which inter-relate with each
other and with their surroundings, and are unlimited in their capabilities to adapt
their behaviour, based on their experience. The relationships that govern social systems are a complex mix of human and non-human interactions. Human interactions
account for relationships and levels of engagement to particular courses of collective
action or to relationships that bind peripheral and central players (Granovetter,
1973; Lin et al., 2001) to each other and to particular tasks. Different modes of interaction reveal the interdependence among agents and their environment.
Interactions allow co-ordination of both actions and schemas in ways that allow
homogeneity to emerge out of heterogeneity. Such co-ordination mechanisms are
not only restricted to the sharing of knowledge and information but also to the differences within and between communities of actors depending on their identity,
power and interests (Thompson, 1967; Bechky, 2003; Carlile, 2002). Interaction is
also trans-action in that it always reveals the ongoing negotiation and effort towards
reaching common understanding. Inter-dependence cannot be taken for granted
nor can interaction for that matter. Both processes sensitize us to the intertwined
nature of agents across different levels (individual, group, institutions) and the ways
each potentially affect each other as they seek to pursue their interests in community. Consequently, a decision or action by any agent may affect related individuals
and systems. Interaction and interdependence highlight that social actors and their
structures are fractal in nature because they can operate as both parts and wholes at
the same time (see Stacey, 2003). It is therefore, important to understand the conditions that shape or dismantle the inter-relationships that in turn enhance the interdependence between agents. Being sensitive to the multiplicity of interests that are
Antonacopoulou & Chiva: The Social Complexity of Organizational Learning
283
at stake is only one part of the picture. Knowing how to connect diverse interests may
be even more critical (see Antonacopoulou and Méric, 2005).
The interaction between agents extends our current understanding of interdependence beyond information processing (Adler, 1995). The interdependence
between actors brings a strong social and cultural analysis that reveals the tensions
among competing identities, power bases and learning as key dimensions underpinning the way agents and communities of agents negotiate and interact. These
ideas enable us to explore interdependence not only as a matter of a set of relationships but also as a way of co-existence and co-evolution that continuously transforms
both agents, their structures and their interactions.
These principles of social complexity (schemas–diversity and interaction–
interdependence) draw collectively attention to the emergent and fluid nature of
social systems. The emergence of social systems shows that alongside their capacity
to reinforce stability through processes of institutionalization, they are also creating
a multiplicity of possibilities for change through their ongoing evolution.
Emergence and processes of becoming are endemic to self-organization. The latter
reflects the ongoing balancing acts between stability and change, formal and informal structures, short and long-term priorities. This point suggests that evolving
social systems self-organize as they seek to maximize internal co-ordination through
interdependencies, schemas and diversity, at the same time as they seek to enhance
the external co-ordination with the environmental forces.
This means that organizations have a mutually adaptive relationship with their
environment, such that they are not simply trying to adapt to a static environment,
but rather the organization is learning to adapt to an environment that is itself adapting to the market (other organizations and industries) (Anderson, 1999; Axelrod and
Cohen, 1999; Boisot and Child, 1999). On this matter, Stacey (1996: 36) argues that:
As human agents and the systems they make up move around the behavioural loop of discovery, choice and action, they are clearly engaging in a co-evolutionary feedback process in which
what one does affects the others and then returns to affect the first.
Co-evolution also happens between entities within a system, and the rate of their
co-evolution (McKelvey, 1999) is worth considering. Mitleton-Kelly (2003) states that
co-evolution in this context is associated with learning and the transfer of information and knowledge. Learning and knowledge feed the system and mobilize dynamically how existing connections within the system create possible connections across
systems. Connections are coupled with the tensions that accompany them. To connect
is not simply to forge interrelationships—a coming together of multiple elements.
Inter-relationships capture the possible interactions and the resulting interdependencies subject to the ongoing review of collective schemas as the diversity within
and between agents is worked with. These points allow us to understand that not all
complexity is ‘effective’.
According to complexity theorists (e.g. Gell-Mann, 1994; Stacey, 1996) ‘effective
complexity’ may be high or low depending on the creative destruction a system can
accommodate considering the multiplicity of interactions between agents and their
structures. For example, if the dominant schema are consistently modified due to the
ongoing interactions between the agents and their environment then the effective
complexity would be low due to the disorder and the randomness of the environmental forces. A system which is trying to evolve or learn under these circumstances,
284
Management Learning 38(3)
will not be able to extract many regularities, nor will it be able to predict a great deal
in specific terms. Effective complexity is also low when a system operates in an environment that is highly orderly, in the sense that the systems which constitute it,
behave in a perfectly regular manner. In this situation very little happens and little
learning or evolution is possible given that diversity is also limited and the interdependencies between agents become unquestioned and taken for granted.
It could be argued that in social systems effective complexity is sizeable, in conditions that are intermediate between order and disorder (see also Gell-Mann, 1994).
However, Stacey (1996) explains that effective complexity has to be supplemented
by the notion of potential complexity, which is the potential that complex social systems possess to create a great deal of new effective complexity. It could be argued
that potential and effective complexity reveal different ways of learning. Thinking of
organizations as social systems with potential and effective complexity, reinforces the
role of learning as a source of sustainability. These ideas are explored further in the
next section.
Organizational Learning and Social Complexity
Dynamic Learning
When we consider the key principles of social complexity discussed in the preceding
paragraphs we note that learning is a common underlying characteristic. Learning
is central to interdependence as it is the driving force connecting the diverse and
heterogeneous social agents. Learning maximizes diversity by drawing on the distinctive qualities of individual social agents as a basis of identifying complementarities
that can address their common and diverse agendas. Learning provides the energy
for connections to be made and highlights the gaps that exist while it also provides
the scope for bridging these gaps. Moreover, learning shapes the emerging schemas
that define the boundaries of action while it also opens up multiple modes of interaction. Modes of interaction are not only the emerging patterns of thinking and
action, they are also the social structures that are constantly evolving as social actors
become sensitized to new possibilities for learning. These new possibilities are also
central to self-organization, the inherent nature of social systems to renew themselves.
This process of renewal and on-going transformation is made possible because learning, like change, is an integral part of living and working.
Therefore, social complexity emphasizes learning as a central feature of the social
and its evolution. Moreover, learning is central to complexity because it highlights the
connecting forces and the conditions that support their interaction. This perspective
not only captures the fluidity that is so central to social systems, it also challenges us
to explore learning as an integral part of what it means to be a viable system (see Beer,
1972). In other words, learning creates the dynamics that allow self-organization as an
inherent mechanism for reaching internal consistency. This self-organization is possible because tensions within the system and with the external environment provide the
basis for renewing the system through renegotiation of schemas, ongoing interaction
and new platforms for exploring interdependencies. The distinction between internal and external environment does not only refer to the relationship between organization and the environment but also the relationships between individuals with
Antonacopoulou & Chiva: The Social Complexity of Organizational Learning
285
different locus of power, accountability and control. This point is critical as it reinforces the political nature of learning.
The political nature of learning remains one of the biggest challenges in learning research. Researchers who focus on the political nature of learning (Coopey,
1994; Antonacopoulou, 2001; Lawrence et al., 2005) highlight mainly the inequalities of power and control, the tensions between individual and organizational priorities in learning or the different perspectives and motives underlying learning and
knowledge. The political nature of learning clearly illustrates that learning does not
take place in a vacuum. Learning makes the connections stimulated by the signals
received within a given context possible. The signals received, however, are subject
to multiple interpretations which define the actions agents take as they seek to make
learning meaningful. From this perspective, learning can help forge connections
between diverse meanings, schemas and actions among actors and their environment beyond the boundaries of their community by providing purpose to the way
they interact and the basis of which they explore their interdependencies. This point
reveals a key dimension of the political nature of learning—the power of learning to
transform tensions into ex-tensions (see Antonacopoulou, 2006a).
In other words, ex-tensions reflect the elasticity of learning to embrace multiple
possibilities by creating new connection. Inter-relationships between actors and their
environment are not only governed by their action but also by their intensions that
are frequently in-tension. Learning brings at-tension specific possibilities, which are
filtered through current schema but which also transform these very schemas to different degrees at different points in time. This point ultimately suggests that tensions
are not only born out of conflict, power and political differences privileging one
mode of reality over another. Instead, tensions are also attractions to different possibilities. Tensions reveal the political nature of learning to provide elasticity to bend
in different directions that expand current modes of action by embracing possibilities
that were not possible. Learning therefore, has the power to transform the impossible
into possible, the irrelevant into relevant, order to disorder, discontinuity to continuity. This capacity to extend current boundaries through learning accounts for the
dynamism that learning provides to organizing.
The social complexity of learning allows viable connections to be forged between a
diverse set of emerging dimensions that affect action and interaction with others. This
point reasserts the social and political significance of learning as a force that cannot be
controlled. Instead, learning is better understood as a dynamic, complex process, which
is embedded in the ways social forces within systems define the conditions of their interaction. Therefore, to say learning is social and political is to appreciate the multiple
ways in which learning is manifested in action. How and why people act is defined by
their learning and in turn defines their understanding that subsequently guides their
future actions. In short, the political nature of learning is reflective of the emerging tensions and extensions it can accommodate as different connections are explored.
The preceding paragraphs have already discussed the centrality of learning in
social complexity. In the paragraphs which follow we extend our analysis by exploring
the contribution of social complexity principles to understanding OL. In pursuing this
analysis we examine how ideas of Complexity Science can help our investigation of
OL. To place the perspective we seek to explore in the context of the current OL
debate, we first provide a brief overview of the main dimensions that the debate in OL
has so far engaged with.
286
Management Learning 38(3)
Rethinking OL
Our thinking about learning in organizations has unfolded over time highlighting
through the various perspectives that have dominated the debate, either the behavioural aspects (Cyert and March, 1963; Levitt and March, 1988), the cognitive issues
(Duncan and Weiss, 1979; March and Olsen, 1975), the socio-cultural dimensions (Cook
and Yanow, 1996; Lave and Wenger, 1991) and more recently the practice-based view
(Nicolini et al., 2003). There are different ways in which the various perspectives in the
current OL literature can be categorized. There is a tendency that categorizations of
different perspectives create juxtapositions that place one perspective against the other.
This is not our intention in this article. We acknowledge that different perspectives provide us with an opportunity to understand different dimensions of the learning phenomenon. Instead, of a critique therefore, of different perspectives we want to show
how some of the common issues different perspectives of OL share can be connected.
Consistent with our preceding analysis our focus is to reveal the tensions inherent in different conceptualizations of learning. This may also help explain why we
adopt a view that conceptualizes learning as a dynamic emergent process as we
detailed in the previous paragraphs. Existing OL theories do not adequately capture
this perspective which also helps explain why no specific OL theory forms the basis
of our departure. We organize the various perspectives in OL theory under two
broad headings—individual and social theories of OL. In doing so, we do not seek
to oversimplify the diversity of perspectives but to show how the social complexity
view of learning we develop in this article provides a useful basis for extending current conceptualizations of OL.
The individual view considers learning as an individual phenomenon and consequently presents OL through the learning of individual actors. The social view considers that learning is a social phenomenon and consequently presents OL through
communities and groups learning collectively. Both views are important and necessary
as we build a social complexity view of OL. This is consistent with the broad classification we set out earlier in the article drawing on Elkjaer (2004) and her distinction
between how OL theories build on the acquisition metaphor and OL theories build
on the participation metaphor. The acquisition metaphor is consistent with perspectives of OL that focus on the ways individuals acquire knowledge through learning. The participation metaphor is consistent with perspectives on OL that recognize
its socially complex nature and who also seek to understand how engagement in
learning through participation supports the interaction among different aspects of
learning. We discuss the individual and social view of OL to distil the main conceptualizations which underpin our current understanding of OL. We then apply the
principles of social complexity discussed in the previous section in the form of three
key dimensions of the dynamic view of OL.
The Individual and Social Views
A notable group of OL scholars have examined how individuals within organizations
learn (March and Olsen, 1975; Argyris and Schön, 1978; Shrivastava, 1983; Simon,
1991; Dodgson, 1993). The individual view explores OL as individual learning in an
organizational context. Researchers from this perspective focus on individual learning
carried out in organizations and the ways this learning is linked to possible organizational changes. The assumption is that individuals learn and then transfer to
Antonacopoulou & Chiva: The Social Complexity of Organizational Learning
287
others what they know. OL models such as that of Huber (1991) are mainly based
on this view, which examines the existence of several phases (information
acquisition—information dissemination—collective interpretation) originated by
individual learning resulting in the change of organizational memory.
The individual view of OL is linked to psychological learning theories (behaviourist,
cognitivist, humanist). OL is the efficient procedure for the processing, interpretation
and improvement of representations of reality through knowledge. Consistent with
this view, much of the recent knowledge management literature assumes that knowledge can be codified, stored and easily transmitted (for a review see Chiva and Alegre,
2005). When this transmission of knowledge occurs, or is embedded in rules and routines, then organizational knowledge is created (Cohen and Bacdayan, 1994). This
conceptualization of learning, knowledge and their relationship within organizations
is based on a positivist epistemology and fails to capture the multiple modes of knowing in action as social actors interact. A greater focus on such interactions could not
only reveal different modes of knowing but also how the relationship between learning and knowledge can be fundamentally challenged (Antonacopoulou, 2006b).
With the publication of studies by Lave and Wenger (1991) and Brown and Duguid
(1991), the social view of OL emerged. This perspective explores OL as the product of
social interactions, normally in the workplace, and poses an alternative to the dominant individual model that conceives the learning individual as someone who processes information and modifies his or her mental structures. The social perspective
implies that individuals are social actors who together construct an understanding of
what surrounds them and learn from the social interaction within social systems such
as organizations (Gherardi et al., 1998).
According to the social perspective, learning can only be achieved through active
participation (Blackler, 1993), which is constantly being modified. As a result, this
perspective focuses on change, rather than on order and regulations (Elkjaer, 1999).
Rather than attempting to understand what type of cognitive processes or conceptual structures are involved in OL, the social perspective sets out to explain which
type of social context is most suitable for OL, focusing on the group and the community, rather than on the mind of the individual. Therefore, as Elkjaer (2003: 50)
argues, learning is perceived as a continuous activity that cannot be controlled; only
the context can be controlled, thus facilitating OL to a greater or lesser extent. In
accordance with the social perspective, OL is conceptualized as the process of social
construction of shared beliefs and meanings in which the social context plays an
essential role (Berger and Luckmann, 1967).
The political aspects of OL, when interpreted from the individual view are frequently conceptualized as if it were a persistent problem, which has to be eliminated if learning is to take place. From a social perspective, this is utopian, as politics is a characteristic of social relations. If knowledge is socially constructed by individuals and groups it is inevitable that particular interpretations will be supported
by some and rejected by others (Coopey, 1994). Thus, the power relations in an
organization will play a key role in OL as they expose competing perspectives and
degrees of inequality in the way different degrees of knowledge are employed to
support different interests. In these terms, politics and power play a vital role in the
social view of OL as they bring to the fore the role of conflict, internal contradictions and tensions between and within social actors. Power is a means of constructing social relations but not the only source of enacting the social reality.
288
Management Learning 38(3)
The individual and social perspectives of OL are useful in that they encourage
us to pay attention to different aspects of learning. By implication if we adopt either
of these views in isolation we will only see part of the picture of what OL entails.
As we have argued in the previous paragraphs, in our view, if OL is to be fully understood it is important to develop lenses for connecting the various aspects so that the
complexity of the phenomenon can be more fully engaged with. We would argue
therefore for an integrative view of OL which, we argue, reflects better the social
complexity of OL. At the same time we seek to draw attention to the social complexity of OL as we feel that this view reveals the dynamic nature of learning and
organizing.
Social Complexity as a dynamic view of OL
The social complexity perspective of OL explores the underlying schemas about
learning in organizations as well as the diverse ways in which individual learners seek
to engage in learning. The social complexity perspective explores the interactions
between learning actors at different levels (individual, group, organization) and the
wider environment and identifies the conditions that underpin the interdependencies
between them. We unpack the underlying characteristics of the social complexity view
of OL by elaborating on three dimensions which reflect the social complexity discussed in our review of Complexity Science. We present these dimensions as a basis
for capturing the dynamics of learning and organizing.
Dimension 1: Engagement in power and politics
Dimension 2: Multiplicity of learning levels
Dimension 3: Inter-connectivity between internal and external forces
We explore each of these dimensions in more detail.
Engagement in power and politics
OL as a social complex process reinforces the importance of power and politics as a
central characteristic of learning and organizing. Politics reflect the dynamics that are
created during negotiations between agents with different interests. Power is the
medium through which conflicts of interests are ultimately resolved (Morgan, 1997)
and it is also a source of reinforcing the choices agents make in the process of interacting with others. Engagement in power and politics by individual agents is a reflection of their ability to retain their power to maintain their unique qualities even when
they operate in community. This point suggests that political behaviour is not only a
reflection of an attempt to fit in by subordinating one’s preferences in the interest of
collective homogeneity. Quite the contrary, we want to argue that individual agents
can fit in a community also when they exercise their practical judgement in assessing
how to respond to situations. By both following the rules and breaking the rules as
they learn, agents of a complex social system contribute through their actions to
either the stability or renewal of the organization. Engagement as an active process of
collaboration between agents supports the co-ordination that underpins the complexity of changing conditions and allows greater responsiveness to internal and
external forces. This means that the relationship between actors and the relationships
between the organization and its environment shape the forces that influence the
learning that takes place as a condition for their co-evolution.
Antonacopoulou & Chiva: The Social Complexity of Organizational Learning
289
In acknowledging that politics are central to social complexity it is not enough to
accept that politics is a characteristic of the social process. Reconciling competing
perspectives is not what produces the dynamism that underpins social complexity.
Instead, we would argue that social complexity is underpinned by the ongoing negotiations that are embedded in the emergent connections resulting from the attraction
and collaboration between diverse perspectives. The challenge for organizations is
to create a context in which learning is attractive to individual agents so that they can
be more engaged in exploring ways in which they can contribute through their
learning to the ongoing renewal of organizational routines and practices. Only then
can learning become a source of dynamic capability (Zollo and Winter, 2002).
Multiplicity of Learning Levels
Perhaps one of the most debated issues in OL research has been the unit of analysis, which best serves our understanding of OL. Adopting a social complexity
perspective encourages us to seek a more holistic understanding of learning across multiple levels (individual, group, organization, environment, see also Antonacopoulou,
2006c). In other words, adopting a more integrative view enables us to combine the
various levels and units of analysis and acknowledge that it is the inter-relationships
between levels and units of analysis that ought to be our focus, thus enabling us to
better understand how units of analysis constitute each other and form part of the
whole. Understanding inter-relationships calls for more than just attributing strong
or weak ties (Granovetter, 1983) to the way we describe how different dimensions are
linked. Inter-relationships seek to account for the conditions that define the possibility for connectivity to happen.
In our analysis of OL the multiplicity of connections and interdependencies
between actors (human and non-human) and their social structures are subject to
forces such as identity, language, power and politics as key conditions shaping how
diversity is accommodated in relation to the emerging schemas. This point suggests
that OL emerges out of the possible connections and inter-dependencies explored
across multiple levels of analysis. Understanding which are the conditions that make
connections across levels of analysis possible and how they form is a key priority.
Inter-connectivity Between Internal and External Forces
Diversity and heterogeneity are achieved by the relationships between agents and by
the internal agents’ or individuals’ relationships with the outside world. The latter
aspect is not emphasized in the literature on the social perspective, as it is understood that OL results from social interaction in the workplace, without reference to
the environment. Moreover, the sense of community in the situated view of OL
assumes homogeneity and neglects the tensions and conflict inherent in competing
views within a community of practice (c.f. Lave and Wenger, 1991). The reason why
this aspect is not covered in this perspective could be that the environment is used in
the individual view as the main catalyst for OL (Duncan and Weiss, 1979; Shrivastava,
1983), given that it is based on the psychological stimulus–response paradigm (Weick,
1991).
However, from the perspective of social complexity, the relationship agents enjoy
with their environment forms the basis for their learning as in this way agents achieve
290
Management Learning 38(3)
heterogeneous individual schemas, which cause amongst other things the effective
complexity of the system and the emerging self-organization. It is critical therefore,
that OL reflects both the endogenous (internal) and exogenous (external) connections shaping learning as different sets of relationships create both the need and the
context for learning. Equally fundamental are the dynamics resulting from the relationship between internal and external forces as they interact. This approach is more
likely to provide scope for exploring the mutually adaptive relationship between
the organization and the environment. The internal and external environment is
dynamic and permeable not least because of the socio-political dynamics that constitute it.
Discussion and Implications for Future OL Research
The ideas of Complexity Science and the social complexity view of OL present an
alternative perspective in the way we conceptualize OL. At the most basic level the
view of OL as a dynamic complex process draws attention to the multiple levels of
learning (multiplicity) and the possibilities they create for learning, subject to their
inter-connectivity. The inter-connections among multiple levels also emphasize the
need to integrate endogenous and exogenous forces and to pay greater attention
to the dynamics such interactions create. In other words, the social complexity of
OL emphasizes interconnectivity by drawing attention to inter-relationships as key
to understanding the fluid, emergent and self-organizing nature of learning in
organizations.
From this perspective, OL could be defined as ‘the flow of learning possibilities
derived from the multiplicity of connections practitioners in community engage
with, as they constantly reconfigure their (learning) practices’. This means that the
actions and interactions of agents create both learning as a possibility and define the
approach by which agents seek to learn. These characteristics suggest that the social
complexity of OL is a non-linear process that generates the possibility for learning
and maximizes the possibilities from learning. This view suggests that agents participate in a self-organizing manner in creating learning opportunities and in so doing
continually produce emergent patterns of relationships that shape how learning
takes place. Agents co-create the reality into which they are acting and that reality
includes themselves. Therefore, learning mirrors individual and collective schema
which are interdependent.
OL as a complex process reveals the dynamic connections between social actors
as it could be at the same time the context and consequence of these interactions.
This conceptualization of OL opens up a multitude of possibilities for rediscovering learning and organizing across levels and units of analysis, in relation to the
multiplicity of possibilities that the connections between them can co-create. The
social complexity of OL emphasizes that organizing is dynamic due to the ongoing
learning that keeps the organization in tension. This means that OL provides the
basis for renewing organizational capabilities by expanding the ways an organization seeks to achieve internal integration through self-organization at the same
time as it maintains external differentiation through emergence. This point fundamentally suggests that dynamism is what makes dynamic capabilities like learning
important to organizational renewal. Such dynamism is fostered in organizations
Antonacopoulou & Chiva: The Social Complexity of Organizational Learning
291
that embrace social complexity and tensions as a means of maintaining the capacity
to extend their business practices in innovative directions.
This re-conceptualization of OL as a dynamic complex process presents a number of opportunities and methodological challenges for future OL research. At the
most basic level it encourages OL researchers to take into account these issues and
to explore how the complexity of OL can provide a basis for better capturing the
conditions that make OL possible providing a greater analysis of the following
issues:
1. A critique of the political forces that influence how actors engage in the development of schemas that allow knowledge to be shared and collective learning to
emerge.
2. A analysis of the negotiations that govern the interaction between diverse/heterogeneous agents within the organization so that the respective power of individual agents can better explain their actions when they seek to protect their
perspective.
3. An elaboration of the conditions that underpin the interconnections between the
organization and the environment and analysis of the interdependencies
between endogenous and exogenous forces defining what learning is and how it
is enacted by individual agents.
4. An examination of the learning practices of individual agents that influence how
OL emerges as a possibility.
5. A greater understanding of the factors which support or hinder OL as it selforganizes would be particularly important in understanding how learning capability can be developed.
6. A review and critique of the governing assumptions that define what learning is
in organizational contexts and under complex conditions revealing the tensions
and ex-tensions from which learning emerges.
All these issues can usefully form central research questions in future OL research.
They reflect a number of themes that fundamentally invite OL researchers to
rethink the basic assumptions about the ontological and epistemological status of
learning as a phenomenon within the context of organizations, the possibility of OL
as a complex dynamic process and the multiplicity of issues that make learning and
organizing mutually constitutive.
Conclusion
The OL debate appears to have reached a point of stalemate where little progress
seems to be noticeable in terms of some of the prominent questions that still remain
unresolved. Although there seems to be some agreement that emotion, power and
politics are part of the OL process and support learning in the presence of diversity
there is still lack of agreement about how OL takes place and the mechanisms or
processes involved, what factors facilitate its development, or what aspects to look for
when we investigate OL.
This article contributes to the ongoing OL debate by providing a new perspective
for re-conceptualizing learning based on some of the principles of Complexity
292
Management Learning 38(3)
Science. The analysis first extends current conceptualizations of OL by drawing
more attention to the characteristics of social complexity. Based on the principles of
social complexity—schemas–diversity, interaction–interdependence—a new dynamic
view of OL is presented. The social complexity view of OL contributes to our understanding of learning as an emergent, self-organizing process that entails multiplicity,
interconnectivity and engagement in power and politics at its core.
Our analysis outlines several issues that can form key research questions which
deserve further examination. We invite the OL research community to try to imagine organizations as if they were social complex evolving systems and see what might
the consequences of this be for OL. Equally, we invite the research community to
rethink OL as a dynamic complex process itself. The social complexity of OL integrates different levels of learning and emphasizes the importance of the emerging
space for learning in the way internal and external interactions between agents and
their structures co-evolve. Moreover, this co-evolution is underpinned by the sociopolitical dynamics that feed the new possibilities social actors seek to engage with as
multiple and competing priorities are constantly negotiated. The contribution of
this perspective is that it challenges us to rethink the very basic epistemological and
ontological assumptions that underpin our definitions of learning, organizing and
OL. This article is a first step in this direction.
Acknowledgements
The authors would like to acknowledge the support of the ESRC/EPSRC Advanced
Institute of Management Research under grant number RES-331-25-0024 for this
research. The comments of the editors and the three anonymous reviewers are also
gratefully acknowledged.
References
Adler, P. S. (1995) ‘Interdepartmental Interdependence and Coordination: The Case of the
Design/Manufacturing Interface’, Organisation Science 6(2): 147–67.
Anderson, P. (1999) ‘Complexity Theory and Organisation Science’, Organisation Science
10: 216–32.
Antonacopoulou, E. P. (2001) ‘The Paradoxical Nature of the Relationship Between Training
and Learning’, Journal of Management Studies 38(3): 327–50.
Antonacopoulou, E. P. (2006a) ‘Working Life Learning: Learning-in-Practise’, in
E. P. Antonacopoulou, P. Jarvis, V. Andersen, B. Elkjaer and S. Høyrup (eds) Learning, Working
and Living: Mapping the Terrain of Working Life Learning, pp. 234–54. London: Palgrave.
Antonacopoulou, E. P. (2006b) ‘Modes of Knowing in Practice: The Relationship Between
Learning and Knowledge Revisited’, in B. Renzl, K. Matzler and H. H. Hinterhuber (eds)
The Future of Knowledge Management, pp. 7–28. London: Palgrave.
Antonacopoulou, E. P. (2006c) ‘The Relationship Between Individual and Organizational
Learning: New Evidence from Managerial Learning Practices’, Management Learning 37(4):
455–73.
Antonacopoulou, E. P. and Méric, J. (2005) ‘From Power to Knowledge Relationships:
Stakeholder Interactions as Learning Partnerships’, in M. Bonnafous-Boucher and
Y. Pesqueux (eds) Stakeholders and Corporate Social Responsibility —European Perspectives,
pp. 125–47. London: Palgrave.
Antonacopoulou & Chiva: The Social Complexity of Organizational Learning
293
Argyris, C. and Schön, D. (1978) Organisational Learning: A Theory of Action Perspective.
Reading, Mass: Addison-Wesley.
Axelrod, R. and Cohen, M. D. (1999) Harnessing Complexity. New York: The Free Press.
Bechky, B. (2003) ‘Sharing Meaning Across Occupational Communities: The Transformation
of Understanding on a Production Floor’, Organisation Science 14: 312–30.
Beer, S. (1972) Brain of the Firm. London: Penguin.
Berger, P. L. and Luckmann, T. (1967) The Social Construction of Reality. London: Penguin.
Blackler, F. (1993) ‘Knowledge and the Theory of Organisations—Organisations as Activity
Systems and the Reframing of Management’, Journal of Management Studies 30(6):
863–84.
Boisot, M. and Child, J. (1999) ‘Organisations as Adaptive Systems in Complex Environments:
The Case of China’, Organisation Science 10: 237–52.
Brown, J. S. and Duguid, P. (1991) ‘Organisational Learning and Communities-of-Practice:
Toward a Unified View of Working, Learning, and Innovation’, Organisation Science
2: 40–57.
Brown, S. L. and Eisenhardt, K. M. (1997) ‘The Art of Continuous Change: Linking
Complexity Theory and Time-Paced Evolution in Relentlessly Shifting Organisations’,
Administrative Science Quarterly 42: 1–34.
Burnes, B. (2005) ‘Complexity Theories and Organizational Change’, International Journal of
Management Reviews 7(2): 73–90.
Carlile, P. (2002) ‘A Pragmatic View of Knowledge and Boundaries: Boundary Objects in New
Product Development’, Organisation Science 13(4): 422–55.
Cheng, Y. T. and Van de Ven, A. H. (1996) ‘Learning the Innovation Journey: Order out of
Chaos?’, Organisation Science 7: 593–614.
Chiva, R. (2003) ‘The Facilitating Factors for Organisational Learning: Bringing Ideas from
Complex Adaptive Systems’, Knowledge and Process Management 10(2): 99–114.
Chiva, R. (2004) ‘Repercussions of Complex Adaptive Systems on Product Design Management’, Technovation 24: 707–11.
Chiva, R. and Alegre, J. (2005) ‘Organisational Learning and Organisational Knowledge:
Towards the Integration of Two Approaches’, Management Learning 36: 49–68.
Clegg, S. R., Kornberger, M. and Rhodes, C. (2005) ‘Learning/Becoming/Organizing’, Organization 12(2): 147–67.
Cohen, M. D. and Bacdayan, P. (1994) ‘Organisational Routines are Stored as Procedural
Memory: Evidence from a Laboratory Study’, Organisation Science 5: 554–68.
Cohen, M. D. and Sproull, L. (1996) ‘Introduction’, in M. D. Cohen and L. Sproull (eds)
Organisational Learning, pp. ix–xv. California: Sage.
Coleman, H. J. Jr (1999) ‘What Enables Self-Organizing Behaviour in Business’, Emergence 1:
33–48.
Cook, S. and Yanow, D. (1996) ‘Culture and Organisational Learning’, in M. D. Cohen and
L. Sproull (eds) Organisational Learning, pp. 430–59. California: Sage.
Coopey, J. (1994) ‘Power, Politics and Ideology’, in J. Burgoyne, M. Pedler and T. Boydell
(eds) Towards the Learning Company, pp. 42–51. London: McGraw-Hill Book Company.
Cyert, R. M. and March, J. G. (1963) A Behavioural Theory of the Firm. Englewood Cliffs, NJ:
Prentice Hall.
Dodgson, M. (1993) ‘Organisational Learning: A Review of Some Literatures’, Organisation
Science 14(3): 375–94.
Dooley, K. J., Corman, S. R., McPhee, R. D. and Kuhn, T. (2003) ‘Modelling High Resolution
Broadband Discourse in Complex Adaptive Systems’, Nonlinear Dynamics, Psychology, and
Life Sciences 7: 61–85.
Duncan, R. B. and Weiss, A. (1979) ‘Organisational Learning: Implications for Organisational
Design’, in B. Staw (ed.) Research in Organisational Behaviour, pp. 75–123. Greenwich, CT:
JAI Press.
294
Management Learning 38(3)
Easterby-Smith, M., Antonacopoulou, E. P., Lyles, M. and Simms, D. (2004) ‘Constructing
Contributions to Organisational Learning: Argyris and the New Generation’, Management
Learning 35: 371–80.
Elkjaer, B. (1999) ‘In Search of a Social Learning Theory’, in M. Easterby-Smith, J. Burgoyne and
L. Araujo (eds) Organisational Learning and the Learning Organisation, pp. 75–91. London: Sage.
Elkjaer, B. (2003) ‘Social Learning Theory: Learning as Participation in Social Processes’, in
M. Easterby-Smith, and M. Lyles (eds) The Blackwell Handbook of Organisational Learning and
Knowledge Management, pp. 38–53. Oxford: Blackwell Publishing.
Elkjaer, B. (2004) ‘Organizational Learning: The “Third Way”’, Management Learning 35(4):
419–34.
Feldman, M. S. (2004) ‘Resources in Emerging Structures and Processes of Change’, Organisation Science 13(3): 295–309.
Gell-Mann, M. (1994) The Quark and the Jaguar. Adventures in the Simple and the Complex. New
York: WH Freeman.
Gherardi, S., Nicolini, D. and Odella, F. (1998) ‘Toward a Social Understanding of How
People Learn in Organisations’, Management Learning 29: 273–97.
Gnyawali, D. R. and Stewart, A. C. (2003) ‘A Contingency Perspective on Organizational
Learning: Integrating Environmental Context, Organizational Learning Processes, and
Types of Learning’, Management Learning 34(1): 63–89.
Goodwin, B. (1994) How the Leopard Changed its Spots: The Evolution of Complexity. London:
Weidenfeld and Nicholson.
Granovetter, M. (1973) ‘The Strength of Weak Ties’, American Journal of Sociology 78(6): 1360–80.
Granovetter, M. (1983) ‘The Strength of Weak Ties—A Network Theory Revisited’, in D. Marsden
and N. Lin (eds) Social Structure and Network Analysis, pp. 105–30. Beverley Hills, CA: Sage.
Gregersen, H. and Sailer, L. (1993) ‘Chaos Theory and its Implications for Social Science
Research’, Human Relations 46: 777–802.
Griffin, D., Shaw, P. and Stacey, R. (1998) ‘Speaking of Complexity in Management Theory
and Practice’, Organisation 5: 315–39.
Holland, J. H. (1995) Hidden Order. How Adaptation Builds Complexity. Reading, MA: AddisonWesley.
Houchin, K. and MacLean, D. (2005) ‘Complexity Theory and Strategic Change: An
Empirically Informed Critique’, British Journal of Management 16: 149–66.
Huber, G. (1991) ‘Organisational Learning: The Contributing Processes and Literature’,
Organisation Science 2(1): 88–115.
Johnson, J. L. and Burton, B. K. (1994) ‘Chaos and Complexity Theory for Management’,
Journal of Management Inquiry, 3(4): 320–8.
Kauffman, S. A. (1995). At home in the Universe. Oxford: Oxford University Press.
Lave, J. and Wenger, E. (1991) Situated Learning: Legitimate Peripheral Participation. New York:
Cambridge University Press.
Lawrence, T. B., Mauws, M. K., Dyck, B. and Kleysen, R. F. (2005) ‘The Politics of Organisational
Learning: Integrating Power into the 4I Framework’, Academy of Management Review 30: 180–91.
Levitt, B. and March, J. G. (1988) ‘Organisational Learning’, Annual Review of Sociology 14: 319–40.
Lin, N., Cook, K. and Burt, R. (2001) Social Capital: Theory and Research. Aldine De Gruyter.
Luoma, M. A. (2006) ‘A Play of Four Arenas: How Complexity Can Serve Management
Development’, Management Learning 37(1): 101–23.
Lyles, M. A. and Easterby-Smith, M. (2003) ‘Organisational Learning and Knowledge
Management: Agendas for Future Research’, in M. Easterby-Smith and M. A. Lyles (eds)
Handbook of Organisational Learning and Knowledge Management, pp. 639–52. Oxford:
Blackwell Publishing.
March, J. and Olsen, J. (1975) ‘The Uncertainty of the Past: Organisational Learning Under
Ambiguity’, European Journal of Political Research 3: 147–71.
Mathews, K. M., White, M. C. and Long, R. G. (1999) ‘Why Study the Complexity Sciences in
the Social Sciences?’, Human Relations 52: 439–62.
Antonacopoulou & Chiva: The Social Complexity of Organizational Learning
295
McKelvey, B. (1999) ‘Visionary Leadership vs Distributed Intelligence: Strategy, Co-evolution,
Complexity’, EIASM Workshop Proceedings, Brussels.
Miner, A. and Mezias, S. (1996) ‘Ugly Duckling No More: Pasts and Futures of Organisational
Learning Research’, Organisation Science 7: 88–99.
Mitleton-Kelly, E. (2003) Complex Systems and Evolutionary Perspectives on Organisations: The
Application of Complexity Theory to Organisations. London: Elsevier.
Morgan, G. (1997) Images of Organisation. Newbury Park: Sage.
Nicolini, D., Gherardi, S. and Yanow, D. (2003) ‘Introduction: Towards a Practice-Based View
of Knowing and Learning in Organisations’, in D. Nicolini, S. Gherardi and D. Yanow (eds)
Knowing in Organisations: A Practice-Based Approach, pp. 3–31. London: M. E. Sharpe.
Pentland, B. T. and Feldman, M. S. (2005) ‘Organisational Routines as a Unit of Analysis’,
Industrial and Corporate Change 14(5): 793–815.
Sammut-Bonnici, T. and Wensley, R. (2002) ‘Darwinism, Probability and Complexity: MarketBased Organizational Transformation and Change Explained Through the Theories of
Evolution’, International Journal of Management Reviews 4(3): 291–315.
Sherman, H. and Schultz, R. (1998) Open Boundaries. New York: Perseus Books.
Shrivastava, P. (1983) ‘A Typology of Organisational Learning Systems’, Journal of Management
Studies 20: 7–28.
Simon, H. A. (1991) ‘Bounded Rationality and Organisational Learning’, Organisation Science
2(1): 125–34.
Simon, H. A. (1996) The Sciences of the Artificial. Cambridge, Mass: Institute of Technology.
Stacey, R. D. (1993) ‘Strategy as Order Emerging from Chaos’, Long Range Planning 26: 10–17.
Stacey, R. D. (1995) ‘The Science of Complexity: An Alternative Perspective for Strategic
Change Processes’, Strategic Management Journal 16: 477–95.
Stacey, R. D., Griffin, D. and Shaw, P. (2002) Complexity and Mangement: Fad or Radical Challenge
to Systems Thinking? London: Routledge.
Stacey, R. D. (1996) Complexity and Creativity in Organisations. San Francisco: Berret-Koehler
Publishers.
Stacey, R. D., Griffin, D. and Shaw, P. (2002) Complexity and Management: Fad or Radical
Challenge to Systems Thinking? London: Routledge.
Stacey, R. D. (2003) Strategic Management and Organisational Dynamics: The Challenge of
Complexity. Essex: Prentice Hall.
Thietart, R. A. and Forgues, B. (1995) ‘Chaos Theory and Organisation’, Organisation Science
6: 19–31.
Thompson, J. (1967) Organisations in Action. New York: McGraw-Hill.
Tsoukas, H. (1998) ‘Introduction: Chaos, Complexity and Organisation Theory’, Organisation
5: 291–313.
Vince, R., Sutcliffe, K. and Olivera, F. (2002) ‘Organisational Learning: New Directions’,
British Journal of Management 13: s1–s6.
Weick, K. E. (1991) ‘The Nontraditional Quality of Organisational Learning’, Organisation
Science 2(1): 116–23.
Zollo, M. and Winter, S. G. (2002) ‘Deliberate Learning and the Evolution of Dynamic
Capabilities’, Organisation Science 13(3): 339–51.
Contact Addresses
Elena Antonacopoulou is Professor and Director of GNOSIS, University of Liverpool Management School, Chatham Street, Liverpool L69 7ZH, UK.
[email: e.antonacopoulou@liv.co.uk]
Ricardo Chiva is at Universitat Jaume I, Campus del Riu Sec, 12071 Castellón, Spain.
[email: rchiva@emp.uji.es]