Adaptive and Generative Learning Implications From Complexity Theories

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

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



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).



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



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

122 R. Chiva et al.


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



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:

124 R. Chiva et al.


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



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

126 R. Chiva et al.


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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Adaptive and Generative Learning Implications From Complexity Theories