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Adaptive and Generative Learning:Implications from Complexity
Theories
Ricardo Chiva,1* Antonio Grando1 and Joaqun Alegre21Universitat
Jaume I, Campus del Riu Sec, 12071 Castelln, Spain, and
2Universitat de Valncia, Avda. de los Naranjos, s/n, 46022
Valencia, Spain
One of the most important classical typologies within the
organizational learningliterature is the distinction between
adaptive and generative learning. However, theprocesses of these
types of learning, particularly the latter, have not been
widelyanalyzed and incorporated into the organizational learning
process. This paper putsforward a new understanding of adaptive and
generative learning within organizations,grounded in some ideas
from complexity theories: mainly self-organization and impli-cate
order.Adaptive learning involves any improvement or development of
the explicateorder through a process of self-organization.
Self-organization is a self-referentialprocess characterized by
logical deductive reasoning, concentration, discussion
andimprovement. Generative learning involves any approach to the
implicate orderthrough a process of self-transcendence.
Self-transcendence is a holo-organizationalprocess characterized by
intuition, attention, dialogue and inquiry. The main implica-tions
of the two types of learning for organizational learning are
discussed.
Introduction
A human being is part of the whole called by us universe,a part
limited in time and space. We experience ourselves,our thoughts and
feelings as something separate from therest. A kind of optical
delusion of consciousness. Thisdelusion is a kind of prison for us,
restricting us to ourpersonal desires and to affection for a few
persons nearestto us. Our task must be to free ourselves from the
prisonby widening our circle of compassion to embrace allliving
creatures and the whole of nature in its beauty. Thetrue value of a
human being is determined by the measureand the sense in which they
have obtained liberation fromthe self. We shall require a
substantially new manner ofthinking if humanity is to survive.
(Albert Einstein, NewYork Post, 28 November 1972)
In recent years, interest in the concept of organiza-tional
learning (OL) has grown dramatically, generat-ing a great deal of
debate and management research
(Bapuji and Crossan 2004; Easterby-Smith et al.2000). Owing to
its popularity and complexity, it issurrounded by a plethora of
perspectives and views(for a review, see Miner and Mezias 1996;
rtenblad2002; Shipton 2006). One of the most importantclassical
typologies within OL literature is the distinc-tion between
adaptive and generative learning(Argyris and Schn 1974, 1978;
Arthur and Aiman-Smith 2001; Fiol and Lyles 1985; Senge
1990).Although nowadays a myriad of terms are used todescribe these
two concepts of learning, this typologywas most likely introduced
into the OL literature byArgyris and Schn (1974) through their
distinctionbetween single loop and double loop learning. Singleloop
learning permits an organization to maintain itspresent policies or
achieve its present objectives byadjusting or adapting its
behaviors. Double looplearning involves the modification of an
organiza-tions underlying norms, policies and objectives.
Most of the research in our field has mentioned andeven
emphasized the importance of both types oflearning for
organizations (e.g. Fiol and Lyles 1985;Miner and Mezias 1996).
However, few works (e.g.
*Address for correspondence: Ricardo Chiva, AssociateProfessor
in Management, Universitat Jaume I, Campus delRiu Sec, 12071
Castelln, Spain. Tel: +34 964 387111; Fax:+34 964 728629; e-mail:
[email protected]
International Journal of Management Reviews (2010)DOI:
10.1111/j.1468-2370.2008.00255.x
2008 The AuthorsJournal compilation 2008 Blackwell Publishing
Ltd and British Academy of Management. Published by
BlackwellPublishing Ltd, 9600 Garsington Road, Oxford OX4 2DQ, UK
and 350 Main Street, Malden, MA 02148, USA
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Argyris et al. 1985; Anderson 1997; Kim 1993;Senge 1990) have
attempted to analyze what factorsfacilitate these activities, have
tried to inquire into theprocess in which they take place or have
incorporatedthese processes into the OL process.
Furthermore,organizations and people are becoming good at
singleloop learning, at adapting to a changing environment,but
practitioners and organizations are not normallyso adept at second
loop learning, at changing theirtheories, models or paradigms. This
may be due toorganizational inertia (Hannan and Freeman 1984)
orindividual resistance to change (Dent and Goldberg1999). Managers
defense mechanisms also mayprevent them from broadening their
beliefs and poli-cies. Most executives are so committed to the
strate-gies and cultures they have nurtured that it is painfulfor
them to admit that these are obsolete (Kets deVries and Miller
1984; Miller 1993). Whatever thecase, generative learning is
generally associated withradical innovations that would
dramatically improvefirm performance (Kang et al. 2007) and that
arebecoming essential in organizations. Consequently,there is still
a need to improve our understanding ofhow double loop or generative
learning takes place inorganizations, where it can be located in
the OLprocess, and how can we enhance it.
According to Tsoukas (1998, 293), the scienceshave historically
set the tone in intellectual inquiry.Furthermore, there seems to be
a fundamental humanurge to want to understand both nature and
society asa unified entity. Tsoukas (1998, 293) justifies
theappearance of a new scientific approach, complexitytheory: If
nature turns out to be much less determin-istic than we hitherto
thought ... then perhaps ourhitherto mechanistic approach to
understanding themessiness we normally associate with the
socialworld may need revising.Tsoukas (1998, 291) statesthat the
Newtonian, traditional or mechanistic style isgradually receding in
favor of the complex, holisticor emergent style, characterized by
the ability tonotice instability, disorder, novelty, emergence
andself-organization. Indeed, an increasing number ofacademics have
started to use complexity theory toaid them in understanding
organizations better.
Complexity theories, generally referring to ideasand concepts at
a distance from the mechanisticview, represent a research approach
that makesphilosophical assumptions about the emerging worldview,
which include wholeness, perspective observa-tion, non-linearity,
synchronicity, mutual causation,relationship as a unit of analysis,
etc. (Dent 1999).The word complexity originates from the Latin
word complexus, meaning comprehension andwholeness; complexity
theories explore the totality(the wholeness) of dynamics forces,
energies, sub-stances and forms permeating the whole universeand
connecting everything that exists in a whirlingweb of dynamic
interrelationships and interactions(Dimitrov 2003).
Complexity theories are increasingly being seen byacademics and
practitioners as a way of understand-ing organizations and
promoting organizationalchange (Burnes 2005, 74). This is so
because com-plexity theories deal with the nature of
emergence,innovation, learning and adaptation (Houchin andMacLean
2005). In spite of the potential importanceof complexity theories
for OL, only a few attemptshave been made to improve our
understanding of OLbased on these ideas (e.g. Antonacopoulou and
Chiva2007; Eijnatten and Putnik 2004; Stacey 1996).However, none of
these papers analyzes or improvesour understanding of adaptive and
generative learningwithin organizations. In this paper, we put
forward anew understanding of the two types of learninggrounded in
some ideas from complexity theories.
Complexity theories serve as an umbrella term fora number of
ideas, theories and research programsthat are derived from a range
of scientific disciplines(Burnes 2005, 73). Consequently, and
according tothis author, there is not one theory, but a number
oftheories (chaos theory, wholeness theory, dissipativestructures,
fractals, complex adaptive systems, etc.)developed by different
scientific disciplines, whichare gathered under the general heading
of complexityresearch. In fact, most of the papers that use
com-plexity theories to aid our understanding of organi-zations
select a few terms, concepts or ideas whichare assumed to be
essential in that analysis (e.g.Houchin and MacLean 2005). In this
paper, we focusmainly on two concepts: self-organization (Gell-Mann
1994; Kauffman 1993); and implicate order(Bohm 1980; Bohm and Peat
2000). These two con-cepts were chosen because they are essential
in learn-ing processes: complex adaptive systems learnthrough a
self-organizing process (Gell-Mann 1994;Kauffman 1993); in
contrast, Bohm (1980) considerslearning and creativity as the
search for and repre-sentation of a new order.
Based on these concepts, we propose and explainsome
characteristics that describe both adaptive andgenerative learning.
Through these characteristics weexplain the process of generative
and adaptive learn-ing and make certain conceptual suggestions to
helpunderstand and foster these processes better. Finally,
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we include both types of learning processes withinthe OL
framework. With the aim of obtainingnew insights from complexity
theories, we follow ametaphorical approach (Houchin and MacLean
2005;Tsoukas 1998; Tsoukas and Hatch 2001), whichavoids searching
for common principles across a vari-ety of very different systems
(physical, social, etc.).
Generative learning is a process that involvessearching for
(implicit) order, which is a holisticunderstanding of anything or
anyone we interactwith (holo-organization). When enacted or
inter-preted (unfolded), this implicate order becomes anew
explicate order, or the manifested world, whichis represented
through mental models, paradigms,etc. Adaptive learning involves
any improvement ordevelopment of the explicate order through
aprocess of self-organization. Generative learning isdeveloped
individually or socially at the edge ofchaos, through intuition,
attention, dialogue andinquiry.
Based on these two conceptualizations, we con-sider learning as
any change (incremental or radical)in the explicate order
(individual or social). Organi-zational learning implies that a new
or improvedorganizational explicate order has been developed.
In pursuing this analysis, we first provide anoverview of the
adaptive and generative learningtypology in the OL literature. We
selected the mainworks that explain their importance, describe
them,analyze their facilitators and incorporate them inthe OL
process. Secondly, we analyze the mainworks that explain the
concepts selected from com-plexity theories: self-organization and
implicateorder. Although we focus mainly on the
complexityliterature, we also take into account
organizationalliterature that has applied complexity ideas. Basedon
these ideas selected from complexity theory, wethen present the
process of generative and adaptivelearning within organizations,
their essential cata-lyzers, and a model of OL that incorporates
bothtypes of learning. Finally, we discuss the mainimplications of
the two types of learning for OL.
Adaptive and generative learning:An OL review
As Shipton (2006, 233) affirms, the study of OL is nolonger in
its infancy. Since the first work in the 1960s(Cangelosi and Dill
1965; Cyert and March 1963),researchers have focused on different
aspects of learn-ing in organizations, in an attempt to find
answers to
questions such as: What does OL mean? How doesOL take place? Who
is learning? What is being learnt?What factors facilitate or
inhibit OL? or Are theredifferent types of OL? In order to improve
under-standing of learning in organizations, differenttypologies
and classifications of OL research havebeen put forward (e.g.
Elkjaer 2004; Miner andMezias 1996; rtenblad 2002; Shipton
2006).Recently, Shipton (2006) analyzed the whole body ofOL
literature through two typologies: prescriptive vsexplanatory and
individual vs organizational.The firsttypology differentiates
between a more prescriptive,normative and practically orientated
literature; and amore explanatory, descriptive, skeptical
literature,centered on understanding the nature and processes
oflearning (Tsang 1997). The second typology exam-ines the level of
analysis: either individual or organi-zational. The former
considers OL to be mainly anindividual activity taking place within
organizationsand that it emerges naturally from day-to-day
prac-tices (Simon 1991). The latter perspective considersOL to be
more than the learning of its individualmembers, and focuses on
systematic and plannedefforts to capture, share and apply the
insights of theindividuals and the groups to which they
belong(Zollo and Winter 2002).
However, one of the most recurring classificationsused by
researchers is the distinction between adap-tive and generative
learning (Senge 1990). Miner andMezias (1996, 88) explain that, in
the OL literature,there are two streams of work: incremental
andradical learning. The former, described by Cyert andMarch
(1963), considers firms as incremental oradaptive learning systems
in which routines and thefirms adapting behavior are essential for
learning(Miner and Mezias 1996). The second stream, basedon Argyris
and Schns (1974, 1978) distinctionbetween single and double loop
learning, stresses theimportance of the latter for organizations.
Singleloop learning implies the ability to detect and correcterrors
in certain operating procedures, whereasdouble loop learning
implies being able to seebeyond the situation and questioning
operatingnorms. Single loop learning is like a thermostat
thatlearns when it is too hot or too cold and turns the heaton or
off (Smith 2001). Single loop learning seems tobe present when
goals, values, frameworks or strat-egies are taken for granted. It
is about efficiency.Double loop learning occurs when error is
detectedand corrected in ways that involve the modificationof an
organizations underlying norms, policies andobjectives (Smith
2001). Miner and Mezias (1996,
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89) point out that most papers support the impor-tance of both
learning streams in organizations.
Argyris and Schns (1974, 1978) distinction wasprobably based on
Ashby (1952) and Bateson (1972),as they proposed similar concepts
of learning. Atalmost the same time as Argyris and Schn,
verysimilar typologies were suggested by authors such asPiaget
(1969), Kuhn (1970) or Watzlawick et al.(1974), among others.
Piaget (1969) discovered thatchildren learn in two different ways.
First, they canlearn through assimilation, when a new fact
isunderstood through a previous model.A different typeof learning
is needed when a new fact cannot beassimilated through a previous
model. In this circum-stance, children need to accommodate or
changetheir model to a new reality. These two kinds oflearning
could be related to single and double looplearning, respectively.
Similarly, Kuhn (1970)describes the evolution of science as a
succession ofparadigm shifts, each one completely reorganizingthe
mental models of the community of practitionersof a certain
scientific field. Kuhn (1970) makes a cleardistinction between what
he calls normal science,where scientists only solve problems by
expandingthe old theory to apply it to new facts, and what hecalls
scientific revolutions, where a scientist createsa completely new
model to explain reality. In the sameway, Watzlawick et al. (1974)
distinguishes betweentwo types of change. First-order changes are
incre-mental changes made within the system, the rules ofwhich are
not changed. In contrast, second-orderchanges imply that the rules
of the system are chal-lenged and changed. They are no longer
changeswithin the system, but changes of the system itself.
Insummary, all the divisions these authors propose showthat this
distinction is generally accepted, not only inthe OL
literature.
Argyris and Schn (1974, 1978) appear to haveintroduced the
distinction between adaptive and gen-erative learning into the OL
literature; however, theyare not the only authors to consider these
types oflearning. Senge (1990), Lant and Mezias (1992),Virany et
al. (1992), Sitkin (1992) or Fiol and Lyles(1985) mention and
analyze the existence of these twotypes of learning in
organizations.
Fiol and Lyles (1985, 807) differentiate betweenlower-level and
higher-level learning. The former is afocused learning that may be
mere repetition of pastbehaviors, adjustments in part of what the
organiza-tion does. Higher-level learning is related to the
devel-opment of complex rules and associations regardingnew
actions.
Senge (1990) distinguishes between adaptive andgenerative
learning. He affirms that generative learn-ing, unlike adaptive
learning, requires new ways oflooking at the world, whether in
understanding cus-tomers or understanding how to manage a
businessbetter. In order to look more deeply into
generativelearning, he introduces the concept of metanoia, aGreek
word meaning a profound shift of mind, whichhe considers to be
synonymous with generative learn-ing. He explains that, for the
Greeks, it meant afundamental change, transcendence (meta)
mind(noia). Senge (1990) affirms that to grasp the meaningof
metanoia is to grasp the deeper meaning of learn-ing, as learning
also implies a fundamental shift ofmind. He compares the everyday
use of learning, suchas taking information or adapting behaviors,
withgenerative learning, and claims that real learning getsto the
heart of what it means to be human. Throughlearning, we recreate
ourselves and perceive the worldand our relationship to it
differently. Generative learn-ing or metanoia refers to a change in
the mentalmodel, paradigm or knowledge through which we seereality.
Recently, Senge et al. (2005) suggested thatgenerative learning
occurs through a process (the Uprocess) that entails three major
stages or elements:sensing, presencing and realizing. Sensing
meansbecoming one with the world, mainly by observing.Presencing
implies a state of becoming totally presentto the larger space or
field around us, to an expandedsense of self, and, ultimately, to
what is emergingthrough us. Realizing involves bringing
somethingnew into reality.
However, OL literature has also described whatstructural or
cultural arrangements are likely to fosterboth adaptive and
generative learning (Anderson1997; Argyris et al. 1985; Senge
1990). Adaptivelearning is related to rationality, defensive
relation-ships, low freedom of choice and discouragement ofinquiry
(Argyris et al. 1985). In contrast, double looplearning is
encouraged through commitment, mini-mally defensive relationships,
high freedom ofchoice and inquiry.
In Senges (1990) view, generative learningrequires five
disciplines: personal mastery, mentalmodels, shared vision, team
learning and systemicthinking. The first, personal mastery, is the
termSenge uses to refer to institutionalized conditions forpersonal
learning within an organization. It is relatedto issues of staff
empowerment and the developmentof staff potentials. Senge explains
that people in anorganization have different internal pictures of
theworld or mental models, the second discipline, which
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should be made explicit so that they can be discussedopenly and
modified. The third discipline, sharedvision, concerns the need for
a certain degree ofconsensus within an organization, and at the
sametime the need for inspiration and motivation. Con-cerning the
fourth discipline, team learning, Sengeexplains that teams, not
individuals, are the funda-mental learning unit in modern
organizations; unlessthe team can learn, the organization cannot
learn.This requires improved interpersonal communica-tion between
team members, a reduction in defensivebehavior, and openness to
creative thinking. The fifthdiscipline, systemic thinking, is
crucial to examinethe interrelationships between parts of an
organiza-tion rather than the parts in themselves. While afocus on
individual parts would only obscure theneed for larger change, a
focus on the whole systemmakes it possible to identify how
organizationalchange might be brought about.
Adaptive and generative learning have not beenextensively
incorporated in frameworks or models forthe process of OL. Kim
(1993) develops a model ofOL that links individual and
organizational levels andalso single and double loop learning
through mentalmodels. However, he recognizes that further work
isneeded for a better understanding of the role ofmental models in
individual and organizational learn-ing, or the types of mental
models that are appropriatefor representing OL dynamic
complexity.
Most of the well-known models (e.g. Crossanet al. 1999; Huber
1991) obviate this typology.Huber (1991) describes four processes
or constructsthat contribute to OL: knowledge acquisition,
infor-mation distribution, information interpretationand
organizational memory. Crossan et al. (1999)developed a framework
for the process of OL thatidentified the role of individuals,
groups and theorganization in feed-forward and feedback
informa-tion flows (Crossan et al. 1999). This frameworkcontains
four related (sub)processes: intuiting, inter-preting, integrating
and institutionalizing, whichoccur over the three levels. Intuiting
and interpretingoccur at the individual level; interpreting and
inte-grating at the group level; and integrating and
insti-tutionalizing at the organizational level. Crossanet al.
(1999) consider that OL is multilevel, and alsothat OL consists not
only of exploring or assimilatingnew learning, but also of
exploiting it or using whathas already been learned
(Cegarra-Navarro andDewhurst 2007; March 1991).
In sum, mention has been made of adaptive andgenerative learning
in the literature of OL since its
first introduction in the field. However, few works(e.g.
Anderson 1997; Argyris et al. 1985; Kim 1993;Senge 1990) have
attempted to analyze what factorsare likely to enable these
activities, have tried toinquire into the process in which they
take place orhave incorporated these processes into the OLprocess.
In fact, this is what Visser (2007) recentlytermed meta-learning.
The aim of this paper is toaccomplish this, essentially through two
conceptsfrom complexity theory: self-organization and impli-cate
order.
Some complexity theories and OL:Self-organization and implicate
order
Complexity theories represent a research approachthat makes
philosophical assumptions of the emerg-ing worldview, which include
holism, perspectiveobservation, non-linearity, synchronicity,
mutualcausation, relationship as unit of analysis, etc. (Dent1999).
Although complexity theories are being usedby an increasing number
of academics to help under-stand organizations, innovation, change
and learning,among other aspects, the application of these
ideasinspired by the physical sciences to the social worldcan often
be controversial. While some authors drawanalogies between
organizations and organisms(Gregersen and Sailer 1993; Stacey 1996;
Thitartand Forgues 1995), others have serious doubts aboutits
applicability, because human systems are not likeother systems in
the physical world (Johnson andBurton 1994). In contrast, Tsoukas
(1998) under-stands that both views are missing the point,
becauseone cannot be certain whether one has captured thenature of
an object of study. He proposes applyingthese ideas to
organizations and seeing what the con-sequences might be (Tsoukas
1998, 305). Similarly,Houchin and MacLean (2005, 152) claim that
the bestuse we can make of complexity theories in under-standing
organization development may be as a meta-phor to give us new
insights, rather than trying tosearch for common principles across
a variety of verydifferent systems (Tsoukas and Hatch
2001).However, this metaphorical approach does not implywe should
ignore the role played by emotions orpolitics, or the options
available to individuals tointerpret, adjust or break rules in
human organiza-tions. These specific characteristics of human
organi-zations need to be considered in order to improve
ourunderstanding of them. This is precisely our approachin this
paper: to obtain new insights from complexitytheories for the study
of OL.
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Complexity is a comprehensive concept for anumber of theories
and ideas that are derived fromscientific disciplines such as
meteorology, biology,physics, chemistry and mathematics (Burnes
2005).Therefore, a group of theories come together underthe general
designation of complexity research.As wementioned above, papers
that focus on these theoriesto advance our comprehension of
organizations indi-cate a few terms or ideas that are assumed to
beessential in their analysis (e.g. Houchin and MacLean2005). In
this paper, we essentially focus on twocomplexity concepts:
self-organization (Gell-Mann1994; Kauffman 1993); and implicate
order (Bohm1980; Bohm and Peat 2000). Below, we brieflydescribe
each of these concepts, and explain why theyare related to learning
within organizations.
Self-organization
Dooley et al. (2003, 62) state that a basic assumptionwithin
complexity theories is that organizations canbe viewed as complex
adaptive systems (e.g. Ander-son 1999; Axelrod and Cohen 1999;
Coleman 1999;Gell-Mann 1994; Houchin and MacLean 2005).These
systems are composed of semi-autonomousagents that seek to maximize
fitness by adjustinginterpretative and action-oriented schema that
deter-mine how they view and interact with other agentsand the
environment (Dooley et al. 2003). Thesesystems are made up of
heterogeneous agents thatinterrelate with each other and with their
surround-ings, and are unlimited in their capabilities to
adapttheir behavior, based on their experience. Conse-quently, they
are complex, in that they are diverseand made up of multiple
interconnected elements,and adaptive in that they have the capacity
to changeand learn from experience. Adaptability is a
systemscapacity to adjust to changes in the environmentwithout
endangering its essential organization.
Complex adaptive systems are capable of antici-pating the
results of their actions, for which theydevelop schemas or models
(Anderson 1999;Holland 1995; Stacey 1996). Each agents behavioris
dictated by a schema, a cognitive structure thatdetermines what
action the agent will take, given itsperception of the environment
(Anderson 1999,219). In organizational systems, agents might
beindividuals, groups or a coalition of groups. Differ-ent agents
may or may not have different schemas,and schemas may or may not
evolve over time(Anderson 1999). Gell-Mann (1994) argues
thatcomplex adaptive systems encode their environments
into many schemas that compete against one anotherinternally.
Changes in agents schemas, interconnec-tion among agents or the
fitness function that agentsemploy produce different aggregate
outcomes.Agents are partially connected to one another, so thatthe
behavior of a particular agent depends on thebehavior of some
subset of all the agents in thesystem. Each agent observes and acts
on local infor-mation only, derived from those other agents towhich
it is connected (Anderson 1999).
Complex adaptive systems continuously self-organize (Anderson
1999; Axelrod and Cohen 1999).Self-organization is a process in
which the internalorganization of a system increases in
complexitywithout being guided or managed by an outsidesource. No
single program or agent completely deter-mines the systems
behavior, which is rather unpre-dictable and uncontrollable
(Goodwin 1994). Patternand regularity emerge without the
intervention of acentral controller. Self-organization is a natural
con-sequence of interactions between simple agents(Anderson 1999).
Although emergence is unpredict-able and uncontrollable, Griffin et
al. (1998, 321)underline that it is intelligible, as we can
perceive thepattern of its evolution. Consequently, not just
any-thing could happen: there is an immanent rationale asto how the
system unfolds a generative process atwork that goes beyond the
correlation of causes andeffects. Although it is not possible to
determine orcontrol results, according to the literature it is
possibleto help self-organization to happen, by facilitating
thehighest effective complexity or the edge of chaos.
Complex adaptive systems are able to develop threetypes of
behavior: stable, unstable or chaotic, andlimited instability or
tension between various forcesthat place them at the edge of chaos.
The edge ofchaos is regarded as a phase change. According
toGell-Mann (1994), this stage represents the highesteffective
complexity. If effective complexity isdefined in terms of the
length of the model, it is lowwhen there is a high level of chaos
and the environ-ment is random, although the algorithmic
informa-tion complexity is very high (Stacey 1996, 96).Effective
complexity is also low when a systemoperates in an environment that
is highly stable, inthe sense that its component systems behave in
aperfectly regular manner. In this situation very littlehappens and
little learning or evolution is needed(Stacey 1996, 96). A complex
adaptive system canlearn only when effective complexity is
sizeable,that is, in conditions that are intermediate betweenchaos
and stability (Gell-Mann 1994).
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Complex adaptive systems evolve over timethrough the entry, exit
and transformation of agentsthat interact and scan their
environment and developschemas. The adaptation of a complex
adaptivesystem to its environment emerges from the adaptiveefforts
of individual agents that attempt to improvetheir own pay-offs
(Anderson 1999). Complex adap-tive systems continuously co-evolve
(Anderson1999; Axelrod and Cohen 1999; Boisot and Child1999), which
means that organizations have a mutu-ally adaptive relationship
with their environment,such that they are not simply trying to
adapt to astatic environment, but rather the organization
islearning to adapt to an environment that is itselfadapting to the
market (other organizations andindustries). McKelvey (1997) has
argued that evolu-tion of organizations cannot be understood
inisolation from the simultaneous evolution of theenvironment. One
characteristic of a complex adap-tive system that is closely
related to connectivity isthe tendency of several systems, or
several sub-systems within one main system, to move togethertowards
new forms of existence or new stages ofdevelopment (Luoma 2006).
This is known asco-evolution. Co-evolution is the mutual
influenceamong systems or agents that become dependent oneach
other. Each party in a co-evolutionary relation-ship exerts
selective pressures on the other, therebyaffecting each others
evolution. Few perfectly iso-lated examples of evolution can be
identified: essen-tially all evolution is co-evolution. Jantsch
(1980),who attributed the entire evolution of the cosmos
toco-evolution, regards co-evolution as an essentialaspect of the
dynamics of self-organization.
Co-evolution also happens among entities within asystem, and the
rate of their co-evolution (Jantsch1980) is worth considering.
Co-evolution can takeplace within an organization, the actors being
anyunits with the ability to interact (Luoma 2006).As thisauthor
maintains, environment is not just everythingthat is not us; it is
a rich collection of other players. Wedo not adapt to some overall
environmental forces;rather, we constantly co-evolve with other
players.
In sum, complex adaptive systems self-organizewhen they are at
the edge of chaos. This implies theevolution of a system into an
organized form in theabsence of external constraints. Adaptability
is oneof the characteristics of complex adaptive systemsthat
implies the systems capacity to adjust tochanges in the environment
without endangering itsessential organization. Adaptive learning is
essentialin these systems.
However, existing schemas can undergo first-orderchange or
single loop learning and second-orderchange or double loop learning
(Dooley 1997; Stacey1996). The former occurs when a system employs
itsschema without change, adapting its behavior to thestimuli
presented to it so that this behavior becomesmore beneficial.
Second-order change or double looplearning occurs when a system
adapts its behavior tothe stimuli presented to it in a beneficial
way as aresult of changing its schema. Schema change gen-erally has
the effect of making the agent more robust(it can perform in the
light of increasing variation orvariety), more reliable (it can
perform more predict-ably), or making it grow in requisite variety
(it canadapt to a wider range of conditions).
In similar terms, Jantsch (1980) explains that, asthe system
reaches beyond the boundaries of its iden-tity, it becomes
creative. This author points out theimportance of
self-transcendence: the creative reach-ing out of a human system
beyond its boundaries.Creation is the core of evolution, which is
the resultof self-transcendence at all levels. Jantsch
(1980)highlights that social systems are re-creative systemsbecause
they can create new reality; socioculturalhuman beings have the
ability to create the condi-tions for their further evolution all
by themselves.Creativity means the ability to create something
newthat seems desirable and helps to achieve definedgoals. By
anticipating the future and creating newreality, social systems
transcend themselves (self-transcendence). Human beings can create
images ofthe future and actively strive to make these imagesbecome
social reality. Individuals can anticipate pos-sible future states
of the world, society as it could beor as one would like it to
become; and they can actaccording to these anticipations. By all
this, Jantsch(1975, 1980) appeared to explain the differencebetween
simply adapting to an environment (adap-tive learning) and creating
a new reality or transcend-ing (generative learning).
Implicate order
Einsteins disciple Bohm (1980) used the theory ofthe implicate
order to present a new model of realitythat contains a holistic
view. It connects everythingwith everything else. In principle, any
individualelement could reveal information about every otherelement
in the universe.
Bohm (1980) developed his theory of the implicateorder to
explain the strange behavior of subatomicparticles, which he
believed might be caused by unob-
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served forces that may be reflective of a deeper dimen-sion of
reality. He calls this reality the implicate order.Bohm (1980) uses
the metaphor of the hologram(Pibram 1991) to explain the implicate
order. He notesthat the hologram illustrates how information
aboutthe entire holographed scene is enfolded into everypart of the
film. It resembles the implicate order in thesense that every point
on the film is completely deter-mined by the overall configuration
of the interferencepatterns. Within the implicate order, everything
isconnected and enfolded into everything else. Thiscontrasts with
the explicate order or manifest worldwhere things are unfolded.The
explicate order derivesfrom the implicate order. This concept is
very muchrelated to Platos theory of forms. Plato suggested thatthe
world as it seems to us is not the real world, butonly a shadow of
the real world, that the world ofappearances (explicate order) is
the shadow of a moreprofound world of forms or ideas (implicate
order).Within the implicate order, there is a totality of
forms,which enfold everything.
Bohm (1980) describes the implicate order as akind of generative
order, which is primarily con-cerned with a deep and inward order
out of which themanifest form of things can emerge creatively.
Infact, he believes there may be an infinite hierarchy ofimplicate
orders. Bohm (2004a) maintains thateverybody has many experiences
of the implicateorder. The most obvious one is ordinary
conscious-ness, in which consciousness enfolds everything thatwe
know or see.
According to Bohm (1980) and Bohm and Peat(2000), to approach
the implicate or generative orderrequires (creative) intelligence,
which is an uncondi-tioned act of perception (intuition) that must
liebeyond any factors that can be included in any know-able law.
Bohm (1980) considers that thought is essen-tially mechanical and
limits perception and intuition.He suggests that the perception of
whether or not anyparticular thoughts are relevant or fitting
requires theoperation of an energy that is not mechanical
energythat we shall call intelligence. He gives an example;one may
be working on a puzzling problem for a longtime. Suddenly, in a
flash of understanding, one maysee the irrelevance of ones whole
way of thinkingabout the problem, along with a different
approach;such a flash is essentially an act of perception.
Simi-larly, Krishnamurti (1994) understands that real learn-ing
brings order and, when learning ceases, it becomesthe mere
accumulation of knowledge (knowing), thendisorder and conflict
begin. He believes that knowl-edge prevents learning.
Bohm (1980) considers that the movement from theexplicate order
to the implicate order and back again,if repeated enough, could
give rise to a fixed disposi-tion. The point is that, via this
process, past formswould tend to be repeated or replicated in the
present,which implies the existence of certain patterns ofvibration
that create the visible forms we see inreality; that implicate
orders influence the externalforms through a process of tuning in,
or morphicresonance (Sheldrake 1981, 1994; Sheldrake et al.2001).
Morphic signifies form, and resonance impliesthe tuning inof two or
more parts into a pattern of thesame frequency. Therefore, it means
tuning in theform (Plato). Through morphic resonance, the pat-terns
of activity in complex systems are influenced bysimilar past
patterns, giving each species and eachkind of system a kind of
collective memory(Sheldrake 1981). It should be noted here
thatSheldrakes concept of morphic resonance blendswith that of
Jungs (1972) theory of synchronicity.Synchronous events or
meaningful coincidencereveal an underlying pattern, a conceptual
frameworkthat encompasses, but is larger than, any of thesystems
which display the synchronicity (Peat 1987).
Bohm (1980) considers that humanity, togetherwith the whole of
the biosphere, is a holistic system.All beings are part of one
consciousness known asimplicate order. All parts are connected with
eachother by frequencies and are in resonance. Frequen-cies,
information and energies are all connected witheach other in
continuous cycle; they all are part of thewhole. If a new impulse
enters into a holistic system,it is effective in all its parts. If
the impulse containsnew core information, a field-like change
occurs thatmakes itself noticed as a mutation, evolutionary leapor
as transformation (generative learning). Suchtransformations occur
in the lives of individuals aswell as in the lives of entire
populations.
The idea is that there is a kind of internal memoryin nature.
Each kind of thing has a collectivememory. Sheldrake (1981) affirms
that systems areshaped by morphic fields, a very similar concept
toimplicate order, which organize atoms, molecules,crystals, cells,
organs, organisms, societies, organi-zations, ecosystems, planetary
systems, solarsystems, galaxies. In other words, they
organizesystems at all levels of complexity, and are the basisfor
the wholeness that we observe in nature, which ismore than the sum
of the parts. Morphic fields alsocontain an inherent memory given
by the process ofmorphic resonance, whereby each kind of thing has
acollective memory. As we have stated, in the human
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realm this is similar to Jungs (1972) theory of thecollective
unconscious. And how that influencemoves across time is given by
the internal processthat Sheldrake (1981, 1994) calls morphic
reso-nance. Morphic resonance suggests that it becomeseasier to
learn what other people have alreadylearned; we all benefit from
what other people havepreviously learned through a kind of
collectivememory, morphic field or implicate order.
Adaptive and generative learningprocesses within
organizations
In the preceding section, we focused on two com-plexity
concepts: self-organization and implicateorder, which we consider
essential for improving ourunderstanding of adaptive and generative
learningprocesses within organizations. In this section, weextend
our analysis by exploring the contribution ofthese ideas to
understanding or rethinking these twotypes of learning and OL
process.
Adaptive and generative learning
In order to explain the different processes of adaptiveand
generative learning within organizations, wepropose the distinction
between complex adaptivesystems and complex generative systems.
Whilecomplex adaptive systems are associated with self-organization
(Anderson 1999), complex generativesystems are related to
self-transcendence (Jantsch1980), which implies a process that
drives agentstowards the implicate order.
One of the chief complexity ideas is the concept ofedge of
chaosor bounded instability, which allowsa system to initiate
change. Organizational systemsmay present three types of states:
stability, chaos andedge of chaos. When the system is stable and
chaotic,effective complexity is low: either because it operatesin
an environment that is highly stable, in the sensethat its
component systems behave in a perfectlyregular manner or because
there is a high level ofdisorder. In both situations little
learning may takeplace (Stacey 1996, 96). However, at the edge
ofchaos, the system is very complex, and finds itself inthe
transition phase between stability and chaos. Inthis situation,
generated through interconnectivityand diversity, (adaptive or
generative) learning mayemerge (Gell-Mann 1994): self-organization
or self-transcendence processes may occur. Neither processcan be
controlled or managed, and results cannot bedetermined in advance,
although certain factors or
conditions might catalyze self-organizing and self-transcendence
processes. Below, we analyze theseconditions and describe the
processes.
Adaptive learning is considered by the OL litera-ture as the
refinement and improvement of existingcompetences, technologies and
paradigms withoutnecessarily examining or challenging our
underlyingbeliefs and assumptions. Complexity literatureunderstands
that complex adaptive systems havethe capacity to adjust to changes
in the environmentwithout endangering their essential
organization.Figure 1 describes the process of adaptive
learningbased mainly on ideas from complex adaptivesystems.
Explicate order, as referred to by Bohm (1980), isthe manifested
world, which is represented throughknowledge, schemas, rules,
mental models, para-digms, etc. Adaptive learning involves any
improve-ment or development of the explicate order through aprocess
of self-organization, which is attained whenthe system is at the
edge of chaos. Self-organization isa self-referential process that
aims to improve orincrease the complexity of the explicate order
withoutbeing guided or managed by an outside source.
Generative learning implies being able to seebeyond the
situation and questioning operating norms(Argyris and Schn 1974).
Senges (1990) concept ofmetanoia describes it as a profound shift
of mind. Aswe mentioned previously, generative learning mightbe
associated with complex generative systems,which self-transcend
(Jantsch 1980) to develop acompletely new order. This process aims
to approachthe implicated order and, to attain this, an
uncondi-tioned act of perception is required (Bohm 1980;Bohm and
Peat 2000). Figure 2 describes the genera-tive learning
process.
The process of self-transcendence (Jantsch 1980)implies going
beyond a certain state or any possible
Figure 1. Adaptive learning
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knowledge (explicate order) and approaching theimplicate order
(Bohm 1980). According to Krishna-murti (1994), learning brings
(new) order. Order is notsynonymous with stability, but is rather a
holisticperception of reality or a new perceptive path
wherepreviously there was only poor or null sensibility.Similar
concepts may include Maslows (1971)notion of peak experienceor the
term alignmentasused by Senge (1990). Maslow (1971) defines
peakexperiences as sudden feelings of intense happinessand
well-being, and possibly the awareness of ulti-mate truth and the
unity of all things. In sum, all theseterms are grounded on the
assumption that parts oftenderive their nature and purpose from the
whole andcannot be understood separately from it.
Moreover,systemically, merely summing individual elementscannot
account for the whole. This is why we alsoconsider that the process
of self-transcendence is aprocess of holo-organization. Within the
implicate
order, everything is connected, everything is in every-thing
else. Thus, we could say that Maslows peakexperience is the
subjective, personal and factualexperience of Bohms holomovement,
his implicitand seamless order revealed to the human
conscience.
As we noted above, self-organization and self-transcendence
might emerge when certain conditionsare in place. In order to
determine these conditionsfor both learning types, we establish
three dimen-sions or levels: individual, social and
impersonal.These dimensions are based on Wilber (2000) andKofman
(2006), who understand that every organi-zation has three
dimensions or realms: the personalor individual realm comprises
psychological orbehavioral aspects (personal values, thinking);
thesocial or interpersonal realm comprises relationalaspects
(relationships, shared values); and finally, theimpersonal realm
comprises technical aspects (tasks,aims) (Table 1).
Adaptive learning is a self-organizational processthat might
happen when individuals and groupswithin organizations mainly
exercise logic or deduc-tive reasoning, concentrate, discuss and
focus onimproving any mental model, knowledge, process,etc.
(explicate order). In contrast, generative learningis a
self-transcendence process that might take placewhen individuals
and groups within organizationsmainly use intuition, attention,
dialogue and aim toquestion any explicate order or knowledge.
Reasoning is the mental process of looking forreasons for
beliefs. Logical deductive reasoning isthe type of reasoning that
proceeds from generalprinciples or premises and, based on those
ideas,derives particular information or deduces the truthabout each
individual part of the whole. Premisesupon which we base our
logical reasoning areaccepted because they are self-evident
truths,which implies that there is no need to question orinquire.
Therefore, it implies taking explicate orderfor granted, and
improving it by reasoning.Figure 2. Generative learning
Table 1. Adaptive vs generative learning
Learning type Adaptive learning Generative learning
Complex system Complex adaptive system Complex generative
systemProcess Self-organization Self-transcendence
(holo-organization)Order Explicate order Implicate orderIndividual,
self (I) Logic deductive reasoning Intuition
Concentration AttentionGroup, social (We) Discussion
DialogueAim, task (It) Improvement Inquiry
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Intuition is defined as a quick and ready insight, aprocess of
coming to direct knowledge without rea-soning or inferring. It is a
way of knowing the truthwithout explanations. Bohm (1980) explains
that, toapproach the implicate order, an unconditioned act
ofperception or intuition is required. Bergson (1946)considers
intuition as a simple, indivisible experi-ence of sympathy through
which one is moved intothe inner being of an object to grasp what
is uniqueand ineffable within it. Bohm (1980) explains intu-ition
as a flash of understanding, in which one seesthe irrelevance of
ones whole way of thinking aboutthe problem, along with a different
approach; such aflash is essentially an act of perception.
Generative learning also requires attention, whichis different
from concentration (Krishnamurti 1994).Concentration is a process
of forcing the mind tonarrow down to a point, whereas attention is
a state inwhich the mind is constantly learning without acenter,
around which knowledge gathers as accumu-lated experience. It
cannot be cultivated through per-suasion, comparison, reward or
punishment, all ofwhich are forms of coercion. The elimination of
fearis the beginning of attention. Fear must exist when-ever there
is an urge to be or to become. Hence,attention arises spontaneously
when the learner issurrounded by an atmosphere of well-being, when
heor she feels secure and at ease. Similarly, Senge et al.(2005)
suggest the importance of observing, becom-ing one with the world.
Consequently, generativelearning is associated with intuition and
attention,whereas adaptive learning is linked to logical deduc-tive
reasoning and concentration.
Isaacs (1993) explains that any conversation flowsto
deliberation, which is to weigh up: consciously orunconsciously
people weigh up different views,finding some with which they agree
and others thatthey dislike. At this point, people face the first
crisis,a decision point that can lead either to discussingviews or
to suspending them (dialogue). Discussionmeans to shake apart, to
analyze the parts (Bohm2004b). Discussion implies dialectic
conversation orthe exchange of arguments and
counter-arguments,respectively advocating propositions (theses)
andcounter-propositions (antitheses). The outcome ofthe exercise
might not simply be the refutation of oneof the relevant points of
view, but a synthesis orcombination of the opposing assertions. The
aim ofthe dialectical method, often known as dialectic
ordialectics, is to try to resolve the disagreementthrough rational
discussion and, ultimately, thesearch for truth or objective
reality. In order to
improve the explicate order (knowledge, paradigm,etc.),
discussions are based on its analysis, byimproving the perception
of reality. Complex adap-tive systems are purposeful, are
determined to act ina certain way, basically to adapt to an
environment,which implies improving the explicate order, toadvance
or make progress in what is desirable.
Bohm (2004b, 7) explains that dialogue is astream of meaning
flowing among and through usand between us. This will allow meaning
to flow inthe whole group, out of which may emerge somenew
understanding. In dialogue, nobody is trying towin; everybody wins
if anybody wins (Bohm2004b). Following Isaacs (2003), dialogue
alsobegins with conversation, but when different viewsappear,
instead of discussing them (dialectic; tobreak apart; to win),
people suspend them (Bohm2004b). They begin to see and explore the
range ofassumptions that are present. For Bohm (2004b),suspending
assumptions implies neither carryingthem nor suppressing them, you
do not believethem, nor do you disbelieve them. This idea can
berelated to the concept of Epoch, a Greek termdeveloped by
Aristotle and, more recently, byHusserl, that describes the
theoretical momentwhere all beliefs are suspended. Similarly,
methodicdoubt, which has become a characteristic method
inphilosophy popularized by Descartes, is a system-atic process of
being skeptical about the truth ofones beliefs. Isaacs (1993, 30)
considers that dia-logue is an attempt to perceive the world
throughnew eyes, not merely to solve problems using thethought that
created them in the first instance. Like-wise, Bohm (1980) and
Krishnamurti (1969, 1974,1994, 2005) consider that knowledge
prevents gen-erative learning. Krishnamurti (1974) considers
thatthe simple acquisition of information or knowledgeis not
learning. Learning is finding out, observing,exploring
relationships.
Dialogue is defined by Isaacs (1993) as a sustainedcollective
inquiry into the processes, assumptionsand certainties that make up
everyday experience. Inorder to learn, Krishnamurti (2005)
maintains thatone needs to be in a state of inquiry, which
requiresa previous state of discontent. Discontent prompts amove to
go beyond the limitations of the actualmodel or tendency. He
proposes questioning orinquiring into everything that has been
accepted.
In sum, adaptive and generative learning carry outdifferent
processes and might be catalyzed or facili-tated by different
factors. Thus, two propositions areput forward:
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Proposition 1: Adaptive learning involves anyimprovement or
development of the explicate orderthrough a process of
self-organization. Self-organization is a self-referential process
character-ized by logic, deductive reasoning,
concentration,discussion and improvement.
Proposition 2: Generative learning involves anyapproach to the
implicate order through a processof self-transcendence.
Self-transcendence is a holo-organizational process characterized
by intuition,attention, dialogue and inquiry.
Organizational learning
Organizing and learning have traditionally been con-sidered
antithetical processes, which qualify OL as anoxymoron (Weick and
Westley 1996, 440).Accordingto this approach, organizing means
ordering, structur-ing and controlling the chaotic world (Watson
1994),and learning is to disorganize and increase variety.However,
Clegg et al. (2005) consider that organizingis not just the process
of managing uncertainty, but isa process of increasing complexity
and reducing it;ordering and disordering are interdependent,
supple-mentary and parasitic. For these authors, learningbecomes
just one element in the process of organizing(Clegg et al. 2005,
155). In our paper, organizing andlearning are considered as
closely linked concepts.Assuggested above, learning involves
creating or search-ing for order (explicate or implicate), and
organizingimplies ordering.
Generative learning is a process that involvessearching for
implicate order, which is a holisticunderstanding of anything or
anyone we interact with(holo-organization). When unfolded,
represented orenacted, this implicate order becomes explicateorder,
or the manifested world, which signifiesmental models, paradigms,
etc. This process ofunfoldment, similar to Crossan et al.s (1999)
inter-preting or Senge et al.s (2005) realizing, consists
ofunfolding the implicate order; making it explicit,applicable,
knowledgeable.
Knowledge is the body of data that comprises ourrational picture
of the world and how to live in it, and,while Krishnamurti (1994)
recognizes its usefulness,he cautions us against focusing too
exclusively on thebuilding-up of knowledge at the expense of
generativelearning, which is a liberation from the limits
ofknowledge. Therefore, generative learning is beyondknowledge,
because the latter is rooted in the past andwould obviously prevent
new things being seen.However, adaptive learning uses and improves
knowl-edge, the explicate order.
Organizations are systems formed by othersystems or agents
(individuals and groups), all ofwhich can be considered social
actors. We considerthat adaptive and generative learning might
happenin any social actor or agent, individuals and groups,which
implies affirming that organizations learnthrough individuals
(Simon 1991), by reasoning-concentration or intuiting-attention and
also throughcommunities (Brown and Duguid 1991), by discuss-ing or
dialoguing. Learning may start in individualsand in relationships,
which means following a com-prehensive view or accepting both
perspectives, indi-vidual and social (Chiva and Alegre 2005;
Elkjaer2004; rtenblad 2002). Similarly, by adopting asocial
complexity perspective, Antonacopoulou andChiva (2007, 289) seek a
more holistic understand-ing of learning across multiple
levels.
When explicate orders from individuals or groupschange, a
process of institutionalization (Crossanet al. 1999) influences the
explicate order of the orga-nization. Crossan et al. (1999, 529)
affirm that OL isdifferent from the simple sum of the learning of
itsmembers. Although individuals may come and go,what they have
learned as individuals or in groupsdoes not necessarily leave with
them. Some learningis embedded in the systems, structures,
strategy, rou-tines, prescribed practices of the organizations,
etc.Finally, when organizational explicate order influ-ences or
affects individual or group explicate order, aprocess of
exploitation (March 1991) takes place.Crossan et al. (1999)
consider this as a feedbackprocess. Consequently, and following
Marchs(1991) terms, the exploration process might in ourmodel take
two modes: a self-organization process(adaptive) and a
self-transcendence process (genera-tive). Figure 3 describes the
whole OL process.
Discussion
The fundamental contribution of this paper is thedevelopment of
an OL theoretical model that incor-porates adaptive and generative
learning processes.This model is essentially based on two concepts
fromcomplexity theories: self-organization (Gell-Mann1994; Kauffman
1993) and implicate order (Bohm1980; Bohm and Peat 2000). Based on
these concepts,both adaptive and generative learning processes
areexplained, and several procedures to catalyze them arealso
proposed. In order to explain the two processesand how they
interact, we propose the distinctionbetween complex adaptive
systems and complex
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generative systems. The former is associated
withself-organization, adaptive learning and explicateorder. The
latter is related to self-transcendence, gen-erative learning and
implicate order.
Propositions 1 and 2 concisely describe adaptiveand generative
learning processes, underlining thecharacteristics that describe
self-organizing and self-transcending processes. The first process
is character-ized by logical deductive reasoning,
concentration,discussion and improvement. The second one is
typi-fied by intuition, attention, dialogue and inquiry.
Theincreasing significance of generative learning fororganizations,
mainly due to the importance of radicalinnovations, could lead
organizations to follow guide-lines that facilitate or foster
intuition, attention, dia-logue and inquiry, which could require a
neworganizational form and management logic that mightbe related,
for instance, to Kofmans (2006) consciousbusiness or Senge et al.s
(2005) presence.
Adaptive and generative learning are considered tohappen in
individuals and in relationships, whichmeans following a
comprehensive view. Complexitytheory seems to support this holistic
approach (e.g.Antonacopoulou and Chiva 2007). However, OLimplies
more than individual-group adaptive and gen-
erative learning processes. In Figure 3, the whole OLprocess is
depicted. The unfoldment of the implicateorder is considered as a
representation, interpretationor enactment. As a consequence a new
individual orgroup explicate order emerges, which might
becomeorganizational explicate order when the former
isinstitutionalized (Crossan et al. 1999). When organi-zational
explicate order affects other individuals orgroups within the
organization, a process of exploita-tion (Crossan et al. 1999;
March 1991) takes place.
We also state that organizing and learning arestrongly linked,
as learning implies the search fororder, which is considered as a
holistic perception ofreality or a new perceptive path where
previouslythere was only poor or null sensibility.
Furthermore,organizing also implies looking for order. Based
onthese ideas, learning and organizing are consideredvery closely
related concepts, as both aim to bringorder. This leads us to
suggest that, when learning,we organize reality in a different way
and, whenorganizing, a process of learning should have takenplace.
Furthermore, the concepts of self-organizingand self-transcendence
highlight that both processes,adaptive and generative learning,
seek to organize, toreach order. This papers approach differs from
pre-
Figure 3. The OL process
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vious works such as those by Weick and Westley(1996), who
consider organizing and learning asopposites, or by Clegg et al.
(2005), who considerlearning as an element of organizing.
In this paper, we suggest that adaptive learningaims to improve
knowledge (explicate order),whereas generative learning implies the
search forimplicate order, which might involve avoiding pre-vious
knowledge. According to Krishnamurti (1974)and Bohm (1980),
generative learning ceases whenthere is only accumulation of
knowledge; generativelearning only occurs when there is no
accumulationat all. In fact, intuition, dialogue, inquiry and
atten-tion imply suspending knowledge. We believe thatthis is an
important statement that stresses thelimited importance, and its
implicit danger, ofknowledge in facing generative learning and,
hence,radical innovations. Most literature has theoreticallyand
empirically stressed the importance of knowl-edge to develop
innovations (e.g. Leonard-Barton1992; Nonaka and Takeuchi 1995).
However,perhaps these innovations were basically incremen-tal
innovations. Based on our theoretical model, wepropose that a focus
on knowledge could representan obstacle to increasing radical
innovations. Webelieve that our proposals on generative and
adap-tive learning might have important implications forthe radical
and incremental innovation literature.
Similarly, the limited importance of knowledge forgenerative
learning might also imply that activitieslike thinking or reasoning
are not so essential for, andmay even be a hindrance to, generative
learning.Krishnamurti (1994) maintains that thinking is thereaction
to what one knows. Knowledge reacts, andthat is what we call
thinking. However, generativelearning underlines the importance of
intuition,inquiry or attention, which relates to concepts
likecreativity or imagination. Perhaps creativeness orintuition has
always been essential for human beings,even more so than
rationality and thinking. Bohm(2004a, 133) believes creativity is
essential not onlyfor science or art, but for the whole of
life:
If you get stuck in a mechanical repetitious order, thenyou will
degenerate. That is one of the problems that hasgrounded every
civilization: a certain repetition ... Manycivilizations vanished
not only because of external pres-sure, but also because they
decayed internally.
Creativity is blocked by a wide range of rigidly heldassumptions
that are taken for granted by society as awhole (Bohm and Peat
2000).
In this paper, we have metaphorically applied somecomplexity
concepts to organizations and specifically
to OL, and adaptive and generative learning. Conse-quently, we
have not tried to search for commonprinciples across a variety of
very different systems(physical, social, etc.), but to find out or
suggest whatthe consequences might be for OL of taking theseideas
into consideration (Houchin and MacLean2005; Tsoukas 1998; Tsoukas
and Hatch 2001).Future research might extend the model, for
instanceby analyzing why certain explicate orders seem toappear
simultaneously in organizations, or the orga-nizational
consequences of stressing generativelearning within organizations.
Future research linesmight also propose developing a scale to
measureadaptive and generative learning within organiza-tions, and
relate it to other aspects or concepts likeinnovation or human
resource management.
In sum, this paper seeks to provide a more holisticand complex
conceptualization of adaptive and gen-erative learning within OL,
challenging us to rethinkthe very basic assumptions that underpin
our defini-tions of learning and organizing, essentiallygrounded in
complexity theories.
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