so what's new about complexity?

SystemsResearchandBehavioralScienceSyst. Res.20, 409^417 (2003)DOI:10.1002/sres.562

& ResearchPaper

So What’s New About Complexity?

Peter J. Murray*

University of Hull, Business School, Hull, UK

This paper addresses the extent to which the insights of what is called the New Sciences(Catastrophe, Chaos and Complexity Theory) for organizational life are novel, and towhat extent they do not take managers much further forward than other theories whichhave questioned a classical or reductionist view of management. The link to theconference theme is provided by a new model which sets out to define four levels ofcomplexity (sic), dependent on the system exclusivity (degree of complexity) andendurance (degree of change).

The past ten years have seen a growth of interest in the insights which ‘managementcomplexity’ claims to provide for modern management. This author has felt uncomfor-table that many of these insights are in fact little different from insights of previouswriters, and that the lack of evidence for their applicability in natural science systems, letalone organizational situations, means that they have little proven value beyond (in somecases powerful) metaphors.

The paper takes a number of the characteristics of complex systems (the phrase willinclude catastrophic and chaotic systems), and the insights which are claimed formanagement complexity, and will relate these to other (non-complexity) writings, in somecases going back over decades. It uses a case study relating to the author’s own experiencein teaching on MBA programmes to demonstrate the value of complexity ideas, but willevaluate these against alternative insights, again demonstrating the relationships betweencomplexity and change. Copyright # 2003 John Wiley & Sons, Ltd.

Keywords complexity; chaos; metaphor; management; education; organization

INTRODUCTION

The past ten years have seen a growth of interestin the insights which the proponents of whatRosenhead (1998) terms ‘management complex-ity’ claims to provide for modern management.Many of these insights appear to be little

different from insights of previous writers,and that the lack of evidence for their applic-ability in natural science systems (discussed byRosenhead, 1998), let alone organizational situa-tions, means that they have little proven valuebeyond (in some cases powerful) metaphors.

The paper will take a number of the character-istics of complex systems (the phrase will includecatastrophic and chaotic systems), and theinsights which are claimed for management

Copyright # 2003 John Wiley & Sons, Ltd.

* Correspondence to: Peter J. Murray, University of Hull, BusinessSchool, Hull HU6 7RX, UK. E-mail: [email protected]

complexity, and will relate these to other (non-complexity) writings, in some cases going backover decades.

It will use a case study relating to the author’sown experience in teaching on MBA pro-grammes to demonstrate the value of complexityideas, but will evaluate these against alternativeinsights, again demonstrating the relationshipsbetween complexity and change.

This paper will therefore put forward twonarratives of complexity, identify a number of itsinsights, and illustrate them by reference to theauthor’s MBA teaching. They will be placed in aframework (Ofori-Dankwa and Julian, 2001) andcompared to other writings which come tosimilar conclusions. This will enable the paperto reflect on the extent to which complexityprovides new insights, or on other possiblebenefits of the complexity approach to organiza-tional complexity and change.

TWO NARRATIVES OF COMPLEXITY

There appear to be two narratives goingthrough the writing on complexity, although thewriters themselves do not always appear to beaware of them.

Both narratives start with the idea (Coveneyand Highfield, 1995, p. 7) that ‘complexity isthe study of the behavior of macroscopic collec-tions of basic but interacting units which areendowed with the potential to evolve in time’,embodying their two principles of non-linearityand evolution.

The narratives seem to diverge on the basis ofthe interaction. One narrative (I will call it‘molecular’) seems to believe that the rules arerelatively few and simple and are the same (ordrawn from a limited set of possibilities) for allunits. It may therefore be likened to the beha-viour of chemical entities that follow well-defined chemical and physical rules whichunderpin the emergence of their macroscopicproperties. The second narrative (I will call it‘network’) seems to assume that the units arecoupled with one another, but that each linkagewill range from strong to weak, and willincorporate elements of positive and negative

feedback. This is more like neural networks, orthe linkages in psychology, where each interac-tion is different, and where the macroscopicbehaviour is much less clearly linked to theindividual interactions.

The ‘Molecular’ Narrative

The molecular view seems to believe that therules are relatively few and simple (or at least of acause-and-effect variety) and are the same (ordrawn from a limited set of possibilities) for allunits—they often refer to Craig Reynolds’ ‘boids’(quoted in Kelly, 1994, p. 15), a computer si-mulation of a flock of birds in flight.

This view leads naturally to the idea of anattractor, or a set of trajectories in which thesystem is contained, so that as the system evolvesit may appear to exhibit random behaviour, butis in fact following one of the trajectories, alongthe lines of the Butterfly attractor, with whichreaders will be familiar.

These rules form the basis of behaviour of aComplex Adaptive System (CAS), whose beha-viour (allowing for requisite variety) thenenables it to grow and survive, generally withinthe attractor.

This narrative approaches fitness in terms ofsome externally generated criteria of success,and would define fitness as the extent to whichthe system can climb up peaks in a ‘fitnesslandscape’, where the height of a peak is seen asthe measure of success. The ability to climbhigher will depend on rules and process inherentin the system which make possible the emer-gence of the order necessary for the ascent. In thesame way that the system is programmed toprogress in some form of attractor, so itsprogramming will enable it to scale peaks insuch a landscape.

This in turn leads on to the dissipative systemsview of self-organization, which holds that theCAS reaches a point of bifurcation, at which itcan (be made to) choose between a path leadingto destruction (generally by remaining at statusquo) or dissolution and renewal with a differentset of rules. The process is characterized byinvocation of the second law of thermodynamics

RESEARCH PAPER Syst. Res.

Copyright � 2003 John Wiley & Sons, Ltd. Syst. Res. 20, 409^417 (2003)

410 Peter J. Murray

(from physics), which is that a closed system willinevitably become less and less ordered, and sothe maintenance of order requires the input ofenergy from outside the system. The system thenself-organizes on the basis of deep structure (thefew and simple rules mentioned before), with theimplication that the transformed organizationwill be successful. The dissipative structure maybe described as one where the CAS is poisedbetween equilibrium and chaos, rather like watermolecules which have a structure between that ofice (which is highly ordered) and steam (which iscompletely disordered).

As an oversimplification, this narrativeappears to me to take a rather positivist approachto complexity, and requires a set of objectiverules to guide the system, and to decide on thesuccess criteria. As such it lends itself to a viewthat much of what occurs in the system can bedetermined in broad terms, even if the detailedbehaviour is unpredictable. This narrativeappears to be the one adopted by Pascale(1999), MacIntosh and MacLean (1999) andHamel (1999), all of whom appear to view thebehaviour of a system as something which anagent (either the consultant or the senior man-ager) can cause to take place, even though theagent may not be able to prescribe the details ofwhat will happen. Thus Pascale (1999, p. 88)describes how a Managing Director at Shell tookaction to ‘gain the organization’s attention (i.e.disturb equilibrium) . . . [his] solution was to cutthrough the organization’s layers and barriers,. . . create a new sense of urgency, and over-whelm the old order’. MacIntosh and MacLean(1999, p. 305) look for ‘the emergence of orderthrough the repeated application of simplerules’.

The ‘Network’ Narrative

The network view seems to assume that the unitsare coupled with one another, but that eachlinkage will range from strong to weak, and willincorporate elements of positive and negativefeedback.

This view would make what determines theshape of the attractor much more difficult to

explain, and would seem to imply that actorswithin the network can actually alter the shape ofthe attractor by their actions, and even shift thesystem to a new attractor.

The idea of fitness will depend on the degree ofcoupling between the units in the network. Themore complicated set of rules will result incomplex behaviour of the system, which willdemonstrate its fitness by the balance between anover-tightly coupled system (which will over-react to inputs from outside its boundary) and atoo loosely coupled system which will notrespond to those inputs.

In this view, the process of self-organizationtakes place at the ‘edge of chaos’, where thesystem is able to poise itself at a position ofoptimum fitness, between the (ultimately stulti-fying) stability of a very loosely coupled system(which damps down all stimuli), and the chaosand unpredictability (and therefore unmanage-ability) of a very rigid system which overreacts toany stimulus. The stable situation might be seenas a simple attractor (perhaps a point or a cycle)or a strange attractor with limited boundaries,while the chaotic situation might be some form ofstrange attractor. In this view, the system willhave to keep altering its position as the twoattractors themselves alter.

It is tempting to suggest that such a networkapproach allows for a more interpretivist view ofcomplexity, in which the interactions representpolitical activities. This narrative seems to me toconvey a much stronger sense of the successcoming from within the system. This narrative isclearly the one adopted by Marion (1999),Lengnick-Hall and Wolff (1999), Stacey (espe-cially in the 3rd, 1999, edition of his book) andZimmermann (1999). These authors project animage of the system as involving the observer, sothat the individual is a participant in the processof network evolution (perhaps one could say ‘isseen as one of the nodes’, rather than standingoutside the network). Stacey et al. (2000) is clearthat his view of complexity has changed, and thathe believes that it is important to think ‘aboutorganizing as highly complex ongoing processesof people relating to each other’ by means of‘complex responsive processes of relating’. Simi-larly, Lengnick-Hall and Wolff (1999, p. 1114)

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SoWhat’s New About Complexity? 411

describe ‘complexity logic’ as depending upon‘thriving in dynamic nonlinear systems that relyon network feedback and emergent relation-ships’, and Zimmermann (1999, p. 44) quotes oneof her respondent’s words: ‘this idea of coevol-ving with our customers is one of the mostpowerful ideas around today’, and describes acomplex adaptive system as having a ‘denselyconnected web of interacting [independent]agents each operating from its own schema orlocal knowledge’.

COMPLEXITY INSIGHTS

From the two narratives above, we can identifyseveral insights, which for convenience havebeen listed in Table 1. Several of these areillustrated briefly in relation to a case studydrawn from the author’s experience of MBAteaching. Readers who are unfamiliar with someof the insights may find it helpful to read some ofthe extensive literature on complexity.

I tend to make extensive use of case studymaterial when introducing management con-cepts to MBA classes. Such classes are generallytaught a management module (such as Strategy,or Management Thought, or Quality) in anintensive period of instruction, lasting a weekfull-time, or over a weekend. In general theteacher will not have met the students before, sothat one is trying to create a rapport with theclass, and identify individual learning needs, atthe same time as delivering the subject.

A key point in the process, typically after threeor four hours of teaching, is when the studentsare presented with a case study, and a set ofquestions that I, as teacher, invite the students toconsider. The invitation is deliberately couched in

non-directive language, so that the students arefaced with considering both what they shoulddiscuss as well as how to do it. My motive insetting poorly structured cases is to communicatemy experience that management issues are notnecessarily clearly defined, and if they are this isfrequently because a senior manager has createdboundaries which provide the definition.

The students typically go into huddles, and sitreading the case study in silence. This is normalfor a short time, but when the silence extendsbeyond say 20 minutes, I feel it necessary tostimulate some discussion by asking a particulargroup whether they are clear on what is required,and perhaps suggesting an approach to definingand then addressing the problem. I am quiteopen that my main objective is to get the group toundergo the process of discussing the case study(and making sense of the theory taught in thelectures).

It seems to me that the progress of each groupcan follow one path (from a collection of several),and although they do not take exactly the samesteps there are several strategies that I adopt tokeep them on what I judge to be the straight andnarrow. Each of these paths is a procession alongthe contours of some kind of attractor (see below),and the result of my effort is to jog the group ontoa different path, possibly heading for a differentattractor. Which attractor the group finallyreaches will appear as one of several outcomes,more or less desirable from my point of view.

It is possible to identify several ways in which Ican use the metaphors listed in Table 1. Forexample, we may consider whether the studentsare progressing in one of several attractors,perhaps the ‘let’s work together to define whichquestion we are going to answer’ attractor, or the‘I don’t know what he wants us to do’ attractor or

Table 1. Contrasting insights into complexity

‘Molecular’ ‘Network’

Basis Simple underlying rules Strong and weak interactionsAttractor System progresses in attractor Interactions alter attractor

Apparent unpredictabilityFitness Fitness landscape Network fitnessSelf-organization Bifurcation Evolution and emergence

Dissipative system Edge of chaos



412 Peter J. Murray

the ‘I’ve had a busy day at work and can’tconcentrate’ attractor. I might then try to bumpthem into the right (from my point of view)attractor or, from a network perspective, I mightinfluence them to change the attractor. Similarly,their fitness to deal with the task may depend onthe rules which they have already internalized,or it may depend on the strength of theinteractions within the group. As a third exam-ple, when they start to self-organize, they mayrequire me to push them in the right direction atthe point of bifurcation, or they may naturallybalance themselves on the edge of chaos.

In a case study, an attractor can be used as ametaphor for the behaviour of a group ofstudents, whose progress depends on theirknowledge of each other from previous experi-ences in the class, and their interest in the subject.The group then operates as if it progresses alongany one of what may appear to be differentpaths, all of which lie in a single attractor. Theessence of the attractor metaphor is that there aremany paths in a strange attractor, that the precisepath depends on the initial conditions, andsometimes small variations in those initial con-ditions can make major differences to the pathfollowed, and that the path itself does not‘appear’ to be ‘attracted’ to anything. In addition,it is impossible to be certain in which attractorthe group are progressing: it may be the ‘let’swork together to define which question we aregoing to answer’ attractor—they may actually bedrawn to the ‘I don’t know what he wants us todo’ attractor or the ‘I’ve had a busy day at workand can’t concentrate’ attractor.

Left to their own devices, a group of studentswill settle down as if it is in an attractor, whichwe assume is relatively stable, and around which(even if it appears to be random) the discussionwill progress. My own intervention will be basedon a judgement whether that attractor is anappropriate one (by which I mean one which willinform the students). However, the next part ofthe attractor metaphor is that the attractor itself isnot necessarily permanent—it depends on theattitudes of the students involved, and it will bechanging as the students are forming theirgroup. If the group appears to be progressingaround an appropriate attractor (in my judg-

ment), well and good, but if they are heading fora less desirable one (say ‘I don’t know what hewants us to do’ or ‘I’ve had a busy day and can’tconcentrate’) then I will intervene and will eithertry to jog the group out of that attractor, or to getinvolved with a process of altering the attractor,or perhaps discovering that they aren’t in theattractor I thought they were.

Using Complexity metaphor, we might see apeak of fitness or success as being where astudent grasped a concept, and was able to use itto provide an insight into the case study. A keyissue is what constitutes the success criterionwhich makes a peak high and therefore desir-able. In my classes, I encourage the students touse some combination of relevant theory andpractice, leading to some well-justified conclu-sions. The balance between these three elementswill have the effect that if one becomes toostrong, then the situation will appear as thoughthe group finds itself on a minor peak or perhapsplateau. Altering the balance between theory anddescription will tend to take the group up a peak,and providing a good justification will increasethe height still further.

One of the dangers of such a landscape is thatthe group may get itself up a peak which doesn’tlead to anywhere higher, say because a goodbalance between theory and practice is actuallyaddressing an irrelevant question. This pointsout the metaphor that in optimizing one’s effortsit is possible to reach a point from whichprogress can only be made by going downhill(say by rethinking one’s approach to a problem),before making any more progress.

At the bifurcation point identified by MacIn-tosh and MacLean (1999) there are two alter-natives. The group can continue along its currentpath, which will mean that students will con-tinue to seek the ‘right’ answer by simplyquoting from the textbook and describing thefacts of the case. Alternatively, it can be set off ona different path, by adopting a more criticalapproach. The group might be given somefurther input (I will go across to a group whoare rather quiet, and ask them whether they havea problem). I can then tell them not to worryabout getting the ‘right’ answer but rather to setout on their own. After a few false starts, the




students discover a different way of looking atthe problem. In doing this they (or we) will havealtered the ‘deep structure’, the rules whichgovern the ‘success’ of their activity. MacIntoshand MacLean (1999) see this process as disturb-ing a comfortable equilibrium, and requiring thenew rules to be relearned. The group gets aninsight (either from itself or more likely by anintervention by me), and then reorganizes itselfaccording to some very basic underlying princi-ples (perhaps the need to make a presentation tothe class within the next two hours).

As a teacher, I am trying to allow my studentsto explore for themselves the relevance of anumber of concepts which are new and wherethe case study gives the freedom to explore out-side the framework of day-to-day pressures. I seethis as balancing on the edge of chaos betweentaking a very positivist approach to the conceptspresented (getting the answer right) and gettingused to a more interpretivist process of question-ing the models, and indeed using them as a lan-guage with which to discuss business problems.

The edge of chaos metaphor may then beapplied as the group deliberately keeping itselfaway from the stable state where students try togive the right answer (either by demonstratingan understanding of the models they have beentaught, or by demonstrating that they havegrasped the factual details of the case). A groupwhich restricts itself to this position is losing (atleast in my view) any opportunity to thinklaterally—it is frozen in a stable state, where itslearning simply repeats the well-trodden pathsof absorbing facts so that they can be reproducedwhen necessary, for example in assignmentquestions or examinations. There is nothingwrong with such an approach when a studentis learning a technical subject—it seems to be memuch less desirable when a student is trying tounderstand something as complex as organiza-tional behaviour.

COMPARING COMPLEXITYAND OTHER WRITINGS

The discussion in the previous section illustratesthe way in which writers on management

complexity make use of its insights to commenton, and in some cases prescribe solutions tomanagement problems. In this section of mypaper, I wish to address the question of whetherthose insights are unique to a complexityapproach, or whether they can come from othersources.

I will follow the typology of Ofori-Dankwaand Julian (2001), who distinguish four levels ofcomplexity in terms of exclusivity (the degree ofhomogeneity of the rules governing the system)and endurance (degree of change in those rules):

Level one complexity (simple)—ContingencyTheoryLevel two complexity (medium)—Organiza-tional life and Time CyclesLevel three complexity (high)—CompetingValues ApproachesLevel four complexity (very high)—Complex-ity and Entrainment Theories (Chaos)

I will attempt to classify each of the complexityinsights into one of their levels, and in so doingdiscuss the originality of the insight in relation toother management writings

Level One Complexity (Simple)—ContingencyTheory

The authors characterize level one by theexistence of a single core concept, which remainsstable.

The following insights from complexity wouldappear to fit in here:

(a) The simple set of rules in the molecularnarrative, as exemplified in the ‘boids’, mayappear to give an idea of complex behaviour,but will tend only to allow a limited set ofoutcomes. As such, they would seem toprovide similar insights to those given bythe ‘laws’ of economics’, that is, to allow forvariation, but without sufficient intrinsicvariation.

(b) The idea of progression in an attractor has asimilar apparent randomness, which is sim-ply the manifestation of the pathway thesystems is following through the chaotic



414 Peter J. Murray

attractor. The insight for, say, a marketingexecutive, is that the system may be behavingas if it will resist any moves she may make toalter, for example, her position in the marketfrom number two to number one. Thisinsight may ‘explain’ how Coca Cola seemsto remain ahead of Pepsi in spite of thelatter’s best efforts.

Level Two Complexity (Medium)—Organizational Life and Time Cycles

The authors characterize level two by theexistence of a single core concept at any onetime, but with time there will be change betweencore concepts.

These insights appear to fit the bill:

(c) The concept of a fitness landscape, in which asystem may ‘seek out’ higher and higherpeaks, but may sometimes need to go downinto a valley before rising again, presupposesthat there are different sets of rules which canbe brought into play. This insight, like thefollowing one, has much to do with the long-standing idea of deciding which business weare in (Drucker, 1954) and being prepared toredefine the role of the organization ingreater or lesser ways.

(d) Similarly, the idea from the network narra-tive that the players in a system can changethe shape of the attractor or move to a newattractor has resonances with Hamel andPralahad’s (1990) idea of the use of corecompetences to stretch (Johnson and Scholes,1999) the market to make profitable use ofcore competences.

(e) A key complexity insight is that of self-organization via bifurcation and dissipativesystems, which MacIntosh and MacLean(1999) have used to discuss their work inorganizational change. Their intervention tochange the underlying rules of behaviour intheir client provides strong resonance withthe Organizational Development interven-tions (for example, French and Bell, 1995)which espouse similar objectives, and may belittle more than another way of saying thesame thing.

Level Three Complexity (High)—CompetingValues Approaches

The authors characterize level three by theimpact of many core concepts, which are notthemselves changing in time.

Insights which appear to fit in here are listedbelow:

(e) The apparent unpredictability of complexsystems, and their sensitivity to initialconditions exemplified in Lorenz’s ‘butterflyattractor’, bear more than a passing resem-blance to some of the aspects of the unpre-dictability of management actions whicharise in many negotiations: it is not alwayspossible to foresee the results of one’s actions,and a variety of feedback loops and otherinterpersonal dynamics capture the idea thatsubtle differences such as the tone of voicecan in some circumstances alter outcomesdramatically.

(f) The fitness of a network, depending on theinteractions between its nodes (the people init) calls to mind the idea of team roles (Belbin,1981). The need for certain roles, and theirrelative importance depending on the task inhand, might well be ‘calculated’ from theinteraction between those roles in practicalsituations.

(g) The insight of evolution and emergence,which is so central to complexity, relatesvery well both to the systems conception ofemergence and to the idea of strategyemerging (Mintzberg, 1994) rather thanbeing deliberately planned.

Level Four Complexity (Very High)—Complexity and Entrainment Theories (Chaos)

The authors characterize level four by the impactof many core concepts, each changing in time.

(h) The idea that the individuals in the group(or organization) might compete politicallyprovides a basis for the insight of self-organization at the edge of chaos: thepotential for an innovative and effectiveoutcome, or for disaster, is mirrored in many




of the discussions of group dynamics, forexample, Tuckman’s (1965) ‘storming, norm-ing, performing, adjourning’.

REFLECTIONS

The four levels of complexity are a convenientway of classifying ‘complexity’ (i.e. chaos andcomplexity) ideas in terms of their level ofcomplexity in Ofori-Dankwa and Julian (2001)terms.

What they show is that some ‘complexity’ideas are actually used in very non-complex(Ofori-Dankwa and Julian, 2001) ways to makesense of organizational issues.

The point here is that complexity ideas offervaluable insights into organizational issues at alllevels of their complexity, but that these insightsare not unique.

To take an extreme example, the idea of the‘boids’, which appear to be very innovative andadaptable but in reality follow simple ruleswhich ‘work’ in quite complicated situations(but fail in others—what if the ‘boids’ can’tmaintain their distances?) is not that far off theideas of the ‘classical’ strategists such as Ansoff(1968) and Drucker (1954), who see successfulstrategy as consisting of some core precepts.

An important issue is that we tend to simplifythe complexity insights just to make them useful.For example, the idea of an attractor gives thefeeling that the world is following a pattern (ifonly we could discern it) and ignores thepossibility that the pattern could be one thatleads to destruction.

Where complexity may have value is in its all-embracingness: that it might provide an over-arching explanation of a wide range of issues inorganizational life, and specifically may be ableto deal with the complexity produced by a largenumber of only partially understood and chan-ging interacting influences.

To this end I consider complexity as havingthree potential impacts, as metaphor, as anexplanatory narrative, and as a mathematicalmodel.

I am convinced of the power of many of themetaphors which can be drawn from complexity,

and it is certain that as the concepts become morefamiliar through the work of Stacey and otherstheir use will increase, especially where theyenable managers (and students of management)to make sense of the effects of a multiplicity ofaltering influences of organizational life. Follow-ing Rosenhead, I am much less certain that the‘new science’ of complexity, which has not yetbeen effectively demonstrated in physical sys-tems, can provide a ‘management complexity’theory. Finally, while noting several demonstra-tions of mathematical chaos in business systems(e.g. Dooley and van der Ven, 1999), I suspect thatthese will only ‘work’ in very specific situations.

At the moment, it is worth rememberingRosenhead’s (1998) concern that complexitytheory has not even been demonstrated in verymany physical systems, so that its applicability tosocial systems cannot be assumed. One may bydefinition use concepts from one area as meta-phors for another, but this is quite different fromclaiming that the concepts describe or predict thesystems to which they are applied.

CONCLUSIONS

At the end of the paper, it is worth summarizingsome of the conclusions reached or hinted at:

(1) It is clear that complexity science provides anumber of useful metaphors, which enablesense-making of many aspects of currentorganizational life.

(2) None of these have been ‘proven’ to work (inthe sense of providing a comprehensivetheory), and it is difficult to see how thetheory could be tested.

(3) Nevertheless, complexity theory is an excitingdevelopment, because it appears (at least tothose of us with a natural or biological scien-ces background), to offer the hope of explain-ing, or at least making sense of, organizationalphenomena which are complicated.

(4) While complexity theory may one dayprovide an over-arching explanation of com-plexity and change in organizations, in manycases its insights appear to be representationsof existing ideas and knowledge.



416 Peter J. Murray

(5) Finally, the framework provided by Ofori-Dankwa and Julian (2001) is a useful tool foranalysing the insights of complexity, and insome cases suggests that they are not all as‘complex’ as might at first appear to be thecase.

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Drucker, P. 1954. The Practice of Management. Heine-mann: Oxford.

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Hamel G. 1999. Opinion strategy innovation and thequest for value. SloanManagement Review 39(2): 7–14.

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Johnson G, Scholes K. 1999. Exploring CorporateStrategy (5th edn). Prentice-Hall: Englewood Cliffs,NJ.

Kelly K. 1994. Out of Control: The New Biology ofMachines. Addison-Wesley: New York.

Lengnick-Hall CA, Wolff JA. 1999. Similarities andcontradictions in the core logic of three strategyresearch streams. Strategic Management Journal 20:1109–1132.

MacIntosh R, MacLean D. 1999. Conditioned emer-gence: a dissipative structures approach to transfor-mation. Strategic Management Journal 20: 297–316.

Marion R. 1999. The Edge of Organization. Sage: London.Mintzberg H. 1994. The Rise and Fall of StrategicPlanning. Prentice-Hall: Englewood Cliffs, NJ.

Ofori-Dankwa J, Julian SD. 2001. Complexifyingorganizational theory: illustrations using timeresearch. Academy of Management Review 26(3): 415–430.

Pascale R. 1999. Surfing the edge of chaos. SloanManagement Review 40(3): 83–94.

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Stacey RD. 1993, 1996, 1999. Strategic Management andOrganizational Dynamics. Pitman: London.

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