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Michael Reiss
Hyper-Coopetition
A complexity-based approach to production management in the New Economy
2003
Prof. Dr. Michael Reiss
UNIVERSITÄT STUTTGART Lehrstuhl für ABWL und Organisation
Keplerstraße 17 70174 Stuttgart
Tel. 0711 / 121-3155 Fax 0711 / 121-2764
[email protected] Internet: http://lfo.uni-stuttgart.de
1. A Complexity View of the New Economy
Production in the New Economy is very often production of services and informational products. This is especially true for that sector of the New Economy that is focused on e-business. Figure 1 illustrates core elements of a production system in one area of e-business, e-learning.
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Fig 1: Production System for E-Learning Several elements or domains of production management (eg. utiliza-tion of Internet technology, virtual network organization, ) are shaped according to the generic features of the New Economy. Fig. 2 illustrates that the New Economy differs from the traditional Old Economy (only) by degree, not principally. Entrepreneurial orienta-tion for example exists in both economies. Still, there is a difference in degree, both quantitative (incidence of entrepreneurs) and qualita-tive (variants of entrepreneurship such as "netpreneurs", "intrapre-neurs", etc).
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Fig. 2: New and Old Economy Various features in Figure 2 signal a high complexity of this sector of the economy in general i.e. also of production management (cf. Fig. 3). Complexity is, to an extent, part of the identity of the New Economy (cf. Wood 2000; Lissack/ Roos 2000; Underwood 2001). This refers both to the specific complexity load (pressure, require-ments, "stress" etc.) in the New Economy and the available com-plexity potential for dealing with this load.
• INTERNET TIME
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• AMBIGUITY TOLERANCE
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Fig. 3: Complexity in the New Economy Potential for managing the complexity load in the New Economy comprises both hard factors (Internet infrastructure, mobile comput-
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ing etc.) and soft factors (culture, self-organization, learning, agility etc). The dual growth option serves as a perfect illustration of com-plexity potential. Take for example typical patterns of growth: since New Economy companies operate embedded in a network, they have two options for growth (cf. Reiss/ Bernecker 2003) to deal with the complexity load of markets (e.g. reaching critical mass on a global market): traditional growth via expansion of node size by having more employees on the one hand or virtual growth by finding new partners (i.e. increasing the number of nodes) on the other hand.
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Fig. 4: Dual Growth Options in Networks In a way, hypercompetition and coopetition represent the essence of complexity requirements in the New Economy context. Hypercom-petition (cf. d'Aveni 1994; Bruhn 1997; Zahn/ Foschiani 2000) com-prises complexity factors such as speed, surprise or shifting the rules of the game. Coopetition (cf. Brandenberger/ Nalebuff 1996; Bengtsson/Kock 2000; Reiss/ Beck 2000) represents a challenging mix of competition and cooperation due to loose network structures which do not exclude multiple engagements in different networks. The combination "Hyper-Coopetition" represents an extremely chal-lenging complexity load. The complexity of strategy is not mitigated by strategic alliances or similar structures that (try to) eliminate competition amongst former competitors by turning to a complexity-
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reducing cooperative structure (according to the motto "If you can't beat them, join them!"). Instead, coopetition underlines an inherent strain due to "competition amongst partners" which is typical of or-ganizational networks (as opposed to traditional cooperative organ-izational structures). Thus, there is an escalation in overall complex-ity (market + strategy + structure + ...) in New Economy companies. Against this background, the thing to do would be to examine the use of models, which work with “complexity” as the input and/or output parameter, for production systems in the New Economy. In models of this type, attempts are made at putting down typical per-formance phenomena in the New Economy (ie excellence, "hype", rise and fall, ups and downs, breakdown etc.) to complexity phe-nomena. A particularly interesting question is whether hyper-complexity is the major reason behind the numerous tombstones in the New Economy: is complexity overload or "overkill" a specific failure factor in the New Economy?
2. Outline of the Complexity Approach
Complexity-based modelling already has a tradition in formal, natu-ral, social and economic sciences. Often, this form of modelling is labelled "complexity theory" (cf. Brown/Eisenhardt 1997; Allen 2001; Allison/Kelly 1999) or less ambitiously "complexity man-agement" (cf. Adam 1998; Ahlemeyer/ Königswieser 1998; Ghara-jedaghi 1999; Lewin/ Regine 2001; Schwenk-Willi 2002), or "com-plexity handling/ mastering" (cf. Jost 2000). To avoid any controversy about whether complexity-based reasoning is a “sci-ence” or (just) an “art”, this mainstream area will be referred to from now on as the "complexity approach". The complexity approach serves as a holistic framework or "umbrella" for various focused types of modelling. It utilizes complexity as a key unit of analysis. The spectrum comprises mass production, risk management, de-scriptive statistics (i.e. methodology to handle mass data), manage-ment of conflicts (between objectives or stakeholders), probability theory, fuzzy set theory and management (cf. Grint 1997), as well as chaos theory (cf. Figure 5). All of these approaches have (specific
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facets of) complexity as a common denominator (cf. Stacey 2000). At the moment only a vague preconception of complexity is used. There is no doubt that more scrutinizing of the complexity concept is needed to answer questions such as "What is meant by the complex-ity of a strategy, a structure or a market?" (cf. chapter 4).
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Fig. 5: Complexity Approach as Holistic Framework As far as its contribution to the theory of the firm is concerned, well thought-out positioning of the complexity approach reveals that it does not replace economic modelling such as the productivity para-digm, information economics, "economic-behavioral amalgam" (bounded rationality), agency theory, property rights theory, corpo-rate governance or transaction cost theory. Rather, it serves as an in-terpolated support stage in a multi-stage modelling process (cf. Fig 6). Within the modelling of transaction processes for example, com-plexity-based modelling cannot furnish optimal combinations be-tween task features and forms of coordination within the market-hierarchy continuum in the way the Coase theorem can. However, the complexity approach can support the systematic search for driv-ers of transaction costs. Most characteristics of transactions such as frequency, specifity, (un)certainty or (in)stability of tasks referred to in transaction cost theory turn out to be complexity features. So the complexity approach supports the identification of the "quantity
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structure" of transaction costs but is incapable of generating any op-timization based on economic valuation, i.e. prices derived from per-formance measures such as costs, profit or cash flow. Interestingly enough, the complexity approach depends on input from economic models, for example when it comes to the "optimal" management of complexity: handling of complexity in the form of decoupling or standardization has cost effects ("complexity management costs") which frequently serve as criteria for choosing the "best" way of management complexity. The same economic reasoning is behind the idea of "optimal complexity": take for instance the notion of an "optimal degree of conflict" which is supposed to stimulate fair-play-based competition without causing the cost-driving effects of interpersonal quarrels, mistrust and hostility. Metcalfe's law of opti-mal network size also belongs to this kind of complexity modelling based on the economic effects of complexity (cf. Weiber 2002, p. 280). There are parallels between the complexity approach and the system approach (cf. Grothe 1997; Stüttgen 1999) - and probably other "transdisciplines" such as the evolution theory (cf. Malik 2000; Hermann-Pillath 2002): all of them represent formal methods of modelling, operating on formal units such as elements, relationships, variety, decoupling, loose coupling, selection, retention, feedback loops, etc. And all of them function as heuristics in the model-generating process, helping to improve economic modelling, eg theories of the firm.
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conjectures
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Fig. 6: Positioning of the Complexity Approach The complexity approach mainly comprises three areas which are focused on different aspects of complexity respectively: Level of complexity: Major interest here is in the level of overall complexity and in optimal complexity. Complexity becomes a prob-lem when the level of complexity is either too high or too low. Course of complexity: Major concern in this area is the response of complexity to certain drivers of complexity - which can be com-plexities themselves. Examples of these drivers include linear growth of complexity or trade-offs between two categories of com-plexities. Complexity becomes a problem for instance when there is over-reaction, such as a complexity escalation comparable to the "butterfly effect" in chaos theory. Composition of complexity: Here focus is on the optimal composi-tion of different types of complexity. These can be relationships be-
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tween complexity load and complexity potential, various domains of complexity (complexity of markets, products and process) or dimen-sions of complexity (eg diversity, discontinuity). Complexity is con-sidered a problem when there are disproportions in the composition of the overall complexity, regardless of the level of total complexity. A generic type of composition model in the economic and social sci-ences is the complexity-based fit model (Emery/ Trist 1965, Child 1972; Roters 1988). In systems theory, Ashby' s law of requisite va-riety (cf. Ashby 1956) represents an archetype of complexity-based reasoning: it postulates the unilateral "complexity" fit between envi-ronment (surrounding systems) and system (i.e. company). Fig. 7 (cf. Picot/ Reichwald/ Wigand 1996, p.14) outlines a type of fit model relevant to organizing production systems in the New Econ-omy (cf. also Fig. 1).
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Source: Picot/ Reichwald/ Wigand 1996 Fig. 7: Complexity Fit between Task Complexity and Coordination Structures There are numerous models of complexity fit. The core fit model of the complexity approach is based on balancing the complexity load
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(need, requirement, burden) and complexity potential (capabilities, resources, capacities and the like for complexity handling). In Figure 8 three areas of fit and misfit reflect the static view of fitting: com-plexity fit signals an adequate balance of load and potential, whereas the two misfit areas are either determined by a surplus potential (e.g. idle CPU capacity, ERP functionalities, entrepreneurial power etc.) or an overload (inability to go the pace of the Internet time, not meeting demands for personalization of services etc.).
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Fig. 8: Generic Fit Model The area of complexity fit is depicted by a corridor, not just a line. This implies the existence of tolerable imbalances in terms of slight overload ("challenge" to mobilize potential), as well as slight surplus ("slack" to respond to unexpected increases in complexity load, cf. Fallgater 1995). From the dynamic perspective, four basic mechanisms or processes for establishing fit (balancing load and potential) exist. On the one hand, "pull" and "cut" processes adjust potential to load by increas-ing ("pull" via growth, diversification etc.) or reducing ("cut") po-tential (e.g. via downsizing or body leasing of surplus manpower). "Push" and "ease" mechanisms on the other hand adjust load to po-
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tential by increasing load (additional projects, more output volume, more variants of a product, higher rate of innovation etc.) or reduc-ing load (e.g. by means of standardization or extending life cycles). Classical fit approaches in the theory of the firm can be remodelled in terms of complexity fit. Merging the strategic fit approach and the complexity approach helps refine the notion of strategic fit. These models of strategic fit belong to the contingeny, congruency or con-sistency approaches in management (cf. Staehle 1999, pp. 60-66; cf. Andrews 1971). The fit between strategy und structure within Chan-dler's model (cf. Schewe 1999; cf. Fig. 9) represents one of the most popular paradigms of "strategic fit".
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Figure 9: Complexity Fit between Strategy and Structure A simple illustration of Chandler's paradigm (Fig. 9) is the complex-ity fit between diversification strategy (e.g. Amazon entering online clothing business) and divisionalized structures. From the static per-spective, this is accomplished by establishing business units (divi-sions) corresponding to business segments (e.g. products, customers and regions on the e-learning market). But even this "simple" com-plexity fitting is challenging: in the case of related diversification for example, the number of business units may be smaller than the
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number of business segments because products can be clustered into product families or categories. From the dynamic complexity viewpoint, the legendary unilateral fitting formula ("structure follows strategy", ) covers both pull and cut complexity processes (eg clearing the intracompany jungle of committees). Likewise, the "strategy follows structure" formula (cf. Miles/Snow 1978) comprises push activities (eg. structure enabling entry into new markets) and ease activities. In addition to the strat-egy-structure fit model, several other two domain-bilateral fit mod-els exist (cf. Venkatraman 1989; Scholz 2000, pp. 112), such as the afore-mentioned Ashby model, the market-structure fit models (Staehle 1999, pp. 433; Ansoff 1965) and the businesses-resources fit models (cf. Albach 1978, p. 709). The outlined state-of-the-art fit model of the complexity approach has a number of limitations and weaknesses. Some of these have to do with the overly simplistic conception of the model. The dynamic fitting process is undetermined since we do not know which of the four mechanisms will be applied, nor the appropriate sequential combinations (eg first pull, then ease, ...), let alone the simultaneous combinations of these mechanisms. To clarify this ad-ditional guidelines are needed: Principles of handling complexity: This could be accomplished by generic maxims for dealing with complexity. Take for example prin-ciples of parsimony or simplicity, from Ockham's razor („What can be done with fewer [...] is done in vain with more“), lean manage-ment and downsizing to the KISS principle (“Keep it simple, stupid” and the "less is more" principle). The plea for simplicity might stem from the need to avoid high overall complexity (aggregated over all domains, such as complexity of strategy plus complexity of struc-ture) in order to prevent a "complexity overkill". Another option is provided by simplex-complex compensation guidelines (cf. Reiss 1992b). These are not devoted to the idea of complexity reduction. Instead they recommend the compensation of complex activities in one area by simplex activities in another area. Finally the afore-
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mentioned idea of "optimal complexity" can serve as a guideline. For example, this kind of reasoning is reflected in the optimal degree of conflict or decoupling as well as in the distinction between (opti-mal) "economies" and (non-optimal) "diseconomies" of scale, speed or scope. It has to be considered that most of these optimization models yield isolated local optima that are valid in one area of com-plexity only: the optimal extent of conflicts is normally estimated without taking into account existing competences in handling con-flicts. Guidelines from sequential planning : Here orientation is derived from procedures developed in integrated planning (cf. Gutenberg 1965, p.146). In the short run this would advocate a complexity level determined by the "bottleneck domain", i.e. minimum sector-potential (capacity for manufacturing, power on markets etc) to deal with complexity. An existing organizational structure might be the restricting factor. In the long run bottlenecks have to be abolished to facilitate a complexity level which matches the complexity load de-manded by markets or provided by competence. To accomplish this, complexity reasoning must rely on economic modelling in terms of costs and benefits of complexity management. For companies in the New Economy, it can be assumed that a possi-ble imbalance of complexity load and complexity potential has to do with the fact that these companies frequently operate as network-embedded "interpreneurs" (Reiß 2000). As individual entrepreneurs these young and small companies (sometimes known as micro-companies or "one-person shows") certainly lack market power, as well as lobbying power. This fact hampers the execution of some ease-procedures, e.g. reducing pressure from powerful customers and competitors. By networking they want to attain virtual size i.e. access to partner resources via loose contracting. However it re-mains unclear whether this networking enables ease-procedures at the customer and competitor level(s). Furthermore, any networking gives rise to an internal demand for coordination reflected in trans-action costs amongst network partners. The resources required for this internal coordination are not available for establishing potential to handle market-driven complexity loads. In other words, pulling-
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procedures to reach a complexity fit may be less successful (ie may not foster a fit). Multi-domain fit models: The bilateral fitting of two respective do-mains (eg strategy and structure) could be determined by fitting processes between other domains (eg. market and strategy) with markets forcing a high level of complexity. This "complex" fitting requires holistic models of complexity fit. Hence, without further refinement of complexity models some fun-damental generic questions concerning complexity fit, such as whether a balance is reached at a low level (cf. Figure 9) of com-plexity (contraction of complexity via ease and cut) or at a high level of complexity (expansion of complexity via pull and push) cannot be answered. Although there are hints of a potential risk of complexity overload in the New Economy (despite the available potential cap-tured in slogans such as "complexity is our business!"), the evidence of complexity reasoning remains vague. Considering this, at least two extensions of the "state-of-the-art" complexity approach are re-quired. Multilateral fit model: Conceiving several isolated two-domain fit models (eg strategy and structure, structure and competence, market and strategy) can only produce bilateral complexity fits between two domains. However, the idea of strategic fit is supposed to cover all domains of management (strategy, structure, culture, systems etc.) simultaneously (cf. Mintzberg 1989, Scholz 2000). So a more holis-tic and hence more complex model of interdomain complexity fit in terms of a multilateral complexity fit is needed, in accordance with "structure follows process follows strategy" or similar principles. Complexity profile fit model: There is no strict empirical evidence for the validity of some standard fitting models, e.g. for the validity of Chandler's formula concerning structure-strategy fit (cf. Schewe 1999). This lack of corroboration leaves room for several random and even contradictory formulas, such as "structure follows struc-ture" or "structure follows fashion" (cf. Rumelt 1974). The holistic complexity approach offers a model of multidimensional intrado-
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main complexity to cope with this problem. Since both structure and strategy have their own multidimensional structural complexity pro-file and multidimensional strategic complexity profile respectively, an appropriate profile fit model has to be conceived. It is obvious that in both cases a more holistic complexity approach is needed, in general, and for the New Economy in particular.
3. Multilateral Complexity Fit
A multi-domain model of complexity fit has to provide a holistic view of all relevant domains, as well as an idea of a multilateral fit between all domains. Figure 10 combines four complexity domains (markets, strategy, structure and competency). Competency com-prises soft factors (such as learning, culture, skills, tolerance for er-rors and conflicts, relationship assets, systemic thinking etc.), as well as hard factors such as process technology (hardware and software), Internet infrastructure, business intelligence and control systems. Both sets of factors foster the requisite infrastructure to cope with external, strategic and structural complexity. Bilateral fitting procedures yield several, normally incompatible suboptima, such as the Chandler-fit, Ashby-fit, and market-structure contingency. The bilateral strategic fits are quite often reached on different levels of complexity: the degree of divisionalisation (num-ber of business units) may be higher when determined via market pull (strategy-structure fit) than via competence push (competence-structure fit). It is very unlikely that the principle of suboptimization guarantees an overall optimum. Instead, sophisticated models are needed to warrant such ambitious fitting. Take for example a strategy-structure-technology fit model fostering a trilateral fit (Miles/ Snow 1978).
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The multilateral interdependency reasoning is based on a multi-stage complexity load/complexity potential interdependency. The model provides an overall fit and not just several isolated bilateral fits. Multilateral interdomain complexity fit obeys the laws of comple-mentarity: high levels of market complexity load are proliferated into high levels of strategy, structure and competence complexity. The same principle holds for a competence-pushed interdependency: competence (derived from the Internet, an integration of ERP soft-ware and CRM software as well as from entrepreneurial culture and self organization) enables powerful organizational structures (flexi-ble response to changes in customer demands and competitor activi-ties, efficient and effective mastering of business processes etc.) which support hypercompetitive strategies to deal with challenging markets.
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Fig. 10: Multilateral complexity fit Multilateral fitting starts with diagnostic activity, i.e. specifying the existing overall misfit. The diagnostic procedure has two starting points. Market-pulled from market complexity load, resulting in the requisite complexity of competence. Competence-pushed from com-petence potential, yielding a permissible level of market complexity. The overall gap between these two fitting operations can be visual-ized in the southwest quadrant of Figure 10. Within this framework,
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the fitting can be performed by applying all four fitting procedures (ie. push, pull, ease and cut) in every sector. This (admittedly) com-plex procedure increases the likelihood of finding an overall multi-lateral fit. It is possible that New Economy companies with a one-sided com-petence-push approach have difficulties finding an overall fit. Whenever e-business is more about "e" than about "business" the pull and cut-options of fitting are not fully exploited. In the produc-tion of e-learning this is typical of companies that are unable to merge technology-based virtual learning products (WBT, CDs, etc.) with traditional face-to-face learning products (print materials, class-room teaching etc.) in a so-called blended learning offering. The multilateral model also covers fit between non-neighboring do-mains in Figure 10. Take for example classical market-structure con-tingencies and businesses-resources fit. These "short circuits", by omitting interpolated domains, must not however mislead managers into forgetting that fitting jobs still have to be done in the omitted domains. If we take the total (overall) complexity viewpoint complementarity triggers an accumulation of complexities (both loads and potentials) within one institutional entity, e.g. a company or a cross-company supply chain: complexity (potential) is the answer to complexity (load). This accumulation may cause fears of a complexity escala-tion resulting in a complexity "overkill" (provoking simplistic com-plexity reduction attempts, cf. Brandes 2002, Trout/Rivkin 1999). However, complementarity inhibits attempts to relax overall com-plexity by applying complex-simplex patterns (Reiss 1993a): for in-stance, there is no way of responding to a complex strategy with simple structures. This would provoke an (interdomain) misfit, or more precisely an overload of (strategic) complexity not covered by the potential of adequate structures. Likewise, neither culture nor computers function as substitutes for structure. Instead these compe-tence factors provide emergent structures, ie powerful "spontaneous orders" such as "invisible hand"-controlled coordination patterns and
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informal networks. These self-organized structures are capable of supporting complex strategies (cf. Rycroft/ Kash 1999). Interdomain complementarity does not exclude however substitu-tional simplex-complex compensation between subdomains of strat-egy or structure or competency. Take for example the classical para-digm of substituting technical resources for human resources as potential for information processing. Likewise, there are substitu-tional relationships between different kinds of loads: with outsourc-ing, the internal complexity of insourcing is replaced by "external" complexity, i.e. contracting with external suppliers. An e-learning-specific arena of passing on load concerns customization which is charaterized by a substitutional relationship between customization by the provider, customization by the customers as "prosumers" (self-customization), and third party customization by intermediaries (cf. Reiss/ Koser 2001).
4. Complexity Profile Fit
Every domain in the multilateral complexity fit model contains dif-ferent aspects of complexity. This intradomain complexity cannot be adequately incapsulated in one single complexity ratio (eg "the" complexity of organizational structure). Organizational networks for instance - a typical form of organizing production in the New Econ-omy (cf. Figure 1; cf. Bellmann/ Mildenberger 1996) - are entities with several complexity features (cf. Fig. 11; cf. Reiss 2001).
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SEVERALLAYERS
HETEROGENOUSRELATIONSHIPS
CONNECTIVITY
OPTIONS FORINTERACTION
DYNAMICS
COOPETITION
OPENNESS
HETERARCHY
Figure 11: Multidimensional complexity of networks Behind the various complexity features such as coopetition, connec-tivity, openness, and the like are four generic dimensions of com-plexity (cf. Fig. 12): multiplicity, variety (not to be confused with Ashby's notion of variety which covers more or less all dimensions of complexity), fuzziness and dynamics (Reiss 1993a). Some ubiqui-tous complexity phenomena address only one complexity dimension while others are linked to more than one MESS dimension at the same time. This is the case, for instance with "hybrids": hybrid cor-porate visions (eg "glocals", a combination of "global" and "local"), hybrid strategies combining two competitive advantages (outpacing cf Gilbert/Strebel 1987, mass customization cf Pine 1983, etc. cf. Jenner 2000, Proff/ Proff 1997), or hybrid organizational structures (alliances, joint ventures, networks etc.) between market and hierar-chy. Hybrids contain multiplicity (overlay of sales contracts, rela-tional contracts, cultural bonds etc. in organizational networks, cf. Reiß 1996); variety (contradictory elements such as "mass" and "customization"); fuzziness (lack of identity, blur of borders cf. Pi-cot/Reichwald/Wigand 2001), and dynamics (instability due to in-ternal tension).
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The four-dimensional MESS model covers a plethora of ubiquitous complexity aspects (cf. Figure 12). Any of these variables ("scale", "conflict", "speed" etc., cf. also Fig. 7) can be considered a load or a potential depending on the respective context and their extent. This, for instance, is the message behind the "economies" and the "dis-economies" of scale, speed or networks. Diversity in terms of "arbi-trage" is a business (not a burden) for many a company. So too is di-versity in terms of conflict for lawyers, and in terms of suboptimal integration of materials flow for logistics providers. The MESS dimensions can be clustered into two groups requiring two distinct, partly contradictory species of competence: integration, where mass phenomena have to be handled by requisite capacity and variety, and flexibility which deals with change phenomena by tol-erating ambiguity and chaos (i.e. patterns of "disorder").
MULTIPLICITY
•Multitude•Numerosity•Holism•Plurality•Volume•Mass•Frequency•Scale•...
MULTIPLICITY
•Multitude•Numerosity•Holism•Plurality•Volume•Mass•Frequency•Scale•...
VARIETY
•Diversity•Heterogenity•Pluralism•Scope•Options•Variance•Divergence•Conflict•Individualization•Personalization•Specifity•...
VARIETY
•Diversity•Heterogenity•Pluralism•Scope•Options•Variance•Divergence•Conflict•Individualization•Personalization•Specifity•...
INTEGRATION“And linkage“
INTEGRATION“And linkage“
COMPLEXITYCOMPLEXITY
FLEXIBILITY“Or linkage“
FLEXIBILITY“Or linkage“
DYNAMICS
•Growth•Versions•Volatility•Instability•Changefulness•Discontinuity•Amplitudes•Rise & Fall•Lifecycles•Chaos•Surprise•Speed•...
DYNAMICS
•Growth•Versions•Volatility•Instability•Changefulness•Discontinuity•Amplitudes•Rise & Fall•Lifecycles•Chaos•Surprise•Speed•...
FUZZINESS
•Ambiguity•Uncertainty•Risk•Randomness•Paradox•Vagueness•Options•Blur•Entropy•Question Marks•Intransparency•Redundancy / Slack•...
FUZZINESS
•Ambiguity•Uncertainty•Risk•Randomness•Paradox•Vagueness•Options•Blur•Entropy•Question Marks•Intransparency•Redundancy / Slack•...
Figure 12: Dimensions of complexity The MESS concept helps elaborate the hitherto vague idea of what is meant by complexity of strategy, structure or markets. Complemen-tary relationships between the MESS dimensions are quite frequent. This results in an escalation (or de-escalation) of total intradomain complexity. In production processes for example, manufacturing
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variants (variety) also requires flexibility: it has an impact on fuzzi-ness ("Which variant is scheduled next"?), and dynamics ("people and/or technical facilities switching from one variant to another"). Likewise, a multi-objective goal system (multiplicity) is bound to create conflict between several logistics targets (variety). Conflict can be the source of instability (dynamics) of the production system. Conversely, low uncertainty can keep instability down. However, intradomain complexity relationships - unlike interdo-main complexity relationships - are not necessarily dominated by the laws of complementarity. Although multiplicity is, by definition, a precondition for variety ("It takes at least two elements to create va-riety"), and likewise fuzziness a precondition for dynamics ("No ambiguity, no change"), there is no such thing as an automatic esca-lation or accumulation of multiplicity, variety, fuzziness and dynam-ics in one domain. In other words, a high level of multiplictiy (e.g. many customers from all over the world) does not automatically trigger a high level of variety (different offers for each region or country). By means of a globalization strategy this multitude of cus-tomers is offered standardized world products ("commodities") with very little geographical diversity ("customization"). Correspond-ingly, uncertainty is not always reflected in dramatic changes (such as discontinuities) in the course of time. Continuous development can be accomplished by stabilizing (e.g. via step-by-step implemen-tation rather than big bang implementation). Finally, diversity in the form of conflict does not necessarily go along with instability and permanent changes, as long as the conflicting interests are uncou-pled (self-sufficiency that minimizes mutual resource dependence) or harmonized on the basis of compromises (making deals). It would seem that with appropriate measures (such as uncoupling, standardi-zation, proactive avoidance of conflicts etc.) the intradomain prolif-eration of complexity can be suppressed. As for the corresponding potentials (integration versus flexibility), substitutional relationship is the rule since integration is connected to big units, monocentric structures, close coupling (via contracts), variants (broad scope of products at a time), centripetal movements, and the like, whereas flexibility is connected to small units, polycentric structures, loose coupling, versioning (updates, upgrades of products in the course of
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time), centrifugal movements (e.g. spin-offs), etc. Still, some hybrid management concepts try to foster both potentials, ie go beyond the either-or-alternative: this holds for hybrid competitive strategies, holding models (integrative corporate center plus flexible business centers) and similar concepts. In a sophisticated intradomain complexity model, markets, strate-gies, structures and competences are represented by their respective (four-dimensional) complexity profiles (MESS profiles). The MESS profile of a matrix organisation - another relevant pattern for orga-nizing production in the New Economy, especially in the form of a project matrix organization - is depicted in Figure 13.
MULTIPLICITY
...
TWO-DIMENSIONAL
...
CONFLICT
INTERMEDIATE STAGE
...
RESPONSIBILITIES
FUZZINESSDYNAMICS
MULTIPLICITY VARIETY
SCATTERED
...
MULTIPLICITY
...
TWO-DIMENSIONAL
...
CONFLICT
INTERMEDIATE STAGE
...
RESPONSIBILITIES
FUZZINESSDYNAMICS
MULTIPLICITY VARIETY
SCATTERED
...
MULTIPLICITY
...
TWO-DIMENSIONAL
...
CONFLICT
INTERMEDIATE STAGE
...
RESPONSIBILITIES
FUZZINESSDYNAMICS
MULTIPLICITYMULTIPLICITY VARIETY
SCATTERED
...actual profiletarget profile
Figure 13 Complexity Profile of a Matrix Organisation In a multidimensional profile approach, complexity fitting is about matching load profiles and potential profiles (complexity profile fit). As a rule, the measurement of correspondance (fit) between load profile and potential profile is accomplished separately in each di-mension, ie no compensation is permitted. Consequently, the overall degree of fit is defined by multiplicity-fit + variety-fit + fuzziness-fit + dynamics-fit. A deficit, say, in personalization cannot be compen-
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sated by speed, nor can (linear) growth patterns (versions of soft-ware products) make up for a lack in diversity. Similarly, an excess potential in mass production is no acceptable substitute for required slack to handle risks. Consequently, testing interdomain fit (eg struc-ture-strategy fit) should be executed on the basis of complexity pro-files and not on the basis of one undifferentiated catch-all ratio. Before the multidimensional measurement procedure is executed, a closer look at the actual complexity profiles is advisable. One dis-covers that not all complexity measures of a matrix organization, network organization, and the like serve as genuine potential to meet the requirements of strategies. Instead, some complexity measures represent excess complexity. This type of complexity is referred to as "hidden" complexity since it is not visible unless a profile view is applied. Hidden complexity of matrix structures is reflected in con-flicts and scattered responsibilities (cf. Reiss 1994b): these are not required to support functional activities and project performance si-multaneously. On the contrary! They absorb energy which is then no longer available for marketing activities since it is used up for inter-nal communication, coordination, as well as "warfare". Responding to complex strategies with a complex matrix structure would appear to be only superficially convincing. The complexity profile model reveals that this line of complexity thought implies a fallacy since existing hidden complexities are ignored. To discover hidden complexity, the actual MESS profile has to be compared to the requisite MESS profile which is derived from other domains, such as strategy or markets (cf. Figure 10). Discrepancies between the two represent either a deficit or an excess in complexity. With excess complexity, a differentiation has to be made between a) over-load and b) surplus potential. How can hidden complexity as an intradomain misfit between required potential and actual com-plexity be accounted for within a multi-domain multilateral fit model (cf. Figure 10)? On the one hand, complexity overload of matrix structures can pull the development of additional competence to deal with matrix-specific conflicts. On the other hand, it can be countered by ease-fitting. In the case of matrix structures this can eventually mean abandoning this structure because of its negative inherent
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complexity. Since the extra load of complexity is not justified by strategic requirements, let alone by market complexity, ease-procedures make more sense. They are also an antidote against the risk of an overall complexity overkill due to hidden complexities in certain domains. The matrix structure example outlined a comparatively simple situa-tion. Tranferring this line of thought to the New Economy has to take into account that all domains are characterized by a high level of complexity. Markets are turbulent and global. Strategies reflect hypercompetition. This implies hybrid competitive strategies (such as "mass customization" or "mass personalization") which are en-abled by competences fostered by the Internet, as well as by people. Production is embedded in organizational networks or virtual com-panies (cf. Figure 1). The complexity profile of organizational net-works (cf. Figure 11) needs thorough scrutinization, especially since there is something akin to a network “ideology” within the scientific community: networks are treated as a myth or magic formula, rather than as an organizational tool (cf. Figure 14).
MULTIPLICITY
CONNECTIVITY
MULTI LAYER
SCOPE
HETEROGENITY
OPENNESS
FLUIDITY
COOPETITION
HETERARCHY
FUZZINESSDYNAMICS
MULTIPLICITY VARIETYMULTIPLICITY
CONNECTIVITY
MULTI LAYER
SCOPE
HETEROGENITY
OPENNESS
FLUIDITY
COOPETITION
HETERARCHY
FUZZINESSDYNAMICS
MULTIPLICITY VARIETY
actual profiletarget profile
Figure 14 Complexity Profile of a Network Organization
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Figure 14 illustrates that networks are highly complex structures with regard to all four MESS dimensions. On the one hand, their in-herent complexity is welcome in terms of connectivity and fluidity. These support both integration (communication channels) and flexi-bility (low entry and exit costs for project networks). On the other hand, coopetition and heterogeneity are mixed blessings: Moderate forms of competition and heterogenity amongst partners foster crea-tivity and efficiency while high levels absorb a great amount of en-ergy and generate high complexity costs. Since networks contain a considerable amount of hidden complexity load this widespread structure could turn out to be another fallacy in complexity man-agement unless the inherent complexity load is covered by compe-tence. Without doubt entrepreneurial competences together with holistic thinking (cf. Ulrich/ Probst 1991) help to cope with complexity chal-lenges hidden in networks. However, application of the complexity profile model to these competences reveals that there is also hidden complexity load to be taken care of. The dark side of self-organization comprises risks of free riding, loafing, moral hazard, fraud and corruption. This is true not only for teams, but also for in-ter-company networks. So it would be both naive and inconsistent with empirical evidence of New Economy failure to assume that competences of New Economy firms are a panacea for all complex-ity troubles. The lesson learned from analyzing the typical structure and compe-tence situation in the New Economy is that these companies can well be the victim of hidden complexity in networks and self-organization.
5. Conclusions and Outlook
With production management in the New Economy the holistic complexity approach furnishes arguments to answer the crucial question "Is hyper-complexity (hyper-coopetition) responsible for New Economy failure?"
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The following arguments advocate a positive answer: the inability of New Economy companies to achieve a complexity fit may be due to deficits in - establishing a multilateral fit between market, strategy, structure
and competence because of a bias towards competence-pushed fitting as well as restrictions for ease-fitting caused by a lack of power. Consequently crucial fitting parameters for attaining a complexity fit are not applied.
- dealing with hidden complexity in organizational networks and competences of self-organization.
Conversely, successful New Economy companies are those which utilize sophisticated complexity management to foster multilateral complexity fit as well as complexity profile fit. High overall complexity per se is not the "killer" of the New Econ-omy. High complexity is no problem as long as there is a balance between complexity load and complexity potential. In other words naive approaches to complexity in terms of reducing the level of complexity are inadequate. As are naive models of strategic fit that ignore hidden complexity. Only by merging state-of-the-art com-plexity reasoning and strategic fit into a holistic multi-domain multi-dimensional complexity approach models to help understand New Economy failure be conceived. Despite these useful contributions to understanding how the New Economy works "seen through complexity glasses", the holistic complexity approach has quite a number of drawbacks and limita-tions. These are primarily the result of existing drawbacks of a) stra-tegic fit modelling and b) complexity modelling. Demarcation of domains: Models of interdomain complexity fit adopt domain segmentation from the strategic fit models. These con-tingency and consistency models assume a clear demarcation be-tween system and environment. Consequently, "structure" stands for internal organization, whereas "market" stands for external struc-
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tures relating a company primarily to its customers and competitors. It is typical of the New Economy that along with blurred boundaries of the firm due to networks, joint ventures, pro-sumers, quasi firms and the like, this demarcation is no longer a fact and "market" no longer an exogenous domain. Instead, management can draw the line between internal and external value-adding processes by changing contracts. This holds for all demarcations in the value net, ie com-pany to customers, to suppliers, to competitors and to complemen-tors (cf. Brandenburger/Nalebuff 1996, p. 16). Interdomain-fitting between marktes and other domains is not only achieved unilaterally by the "strategy follows market" contingency formula (pull and cut-fitting), but also by the "market follows structure" maxims (push and ease-fitting). Complexity metrics: There are undoubtedly some available meas-urements of complexity. The spectrum comprises counting (nu-merosity), N:K-ratios (N=number of agents; K = number of connec-tions between agents, cf. Eisenhardt/ Bhatia 2002, pp. 433-447), statistics of variance, range, standard deviation (variety), probabili-ties and entropy (fuzziness) and volatility (dynamics). Still, the lion's share of complexity can only be measured on ordinal scales, clarify-ing "more" and "less" complexity at best. This also holds for the un-derlying concepts of "fit" and "misfit". They represent measures of meta-complexity incapsulating the discrepancy (variety) between (1st order) complexity parameters. This lack of "operationalisation" is a major drawback of complexity reasoning, especially when it comes to "estimating" fit or misfit. It would seem that the complexity approach is only capable of provid-ing a number of "soft heuristics". These may be unacceptable from the "Only what is measured can be managed" viewpoint. They are at any rate useful in fostering awareness for the impact of complexity, and they support economic modelling. References
27
Adam D (ed, 1998) Komplexitätsmanagement, SzU – Schriften zur Unternehmensführung, Vol. 61, Wiesbaden 1998
Ahlemeyer HW, Königswieser R (eds, 1998) Komplexität managen: Strategien, Konzepte und Fallbeispiele, Wiesbaden 1998
Albach H (1978) Strategische Unternehmensplanung bei erhöhter Unsicherheit. In: Zeitschrift für Betriebswirtschaft, 41: 702-715
Allen P (2001) What Is Complexity Science? Knowledge of the Limits to Knowledge. In: Emergence – Journal of Complexity Issues in Organizations and Management, 3: 24-43
Allison MA, Kelly S (1999) The Complexity Advantage: How the Science of Complexity Can Help Your Business Achieve Peak Per-formance, New York (NY) 1999
Anderson P (1999) Complexity Theory and Organization Science. In: Organization Science, 10: 216-232
Andrews K (1971) The Concept of Corporate Strategy, Homewood (IL) 1971
Ansoff HI (1965) Corporate Strategy: An analytic approach to busi-ness policy for growth and expansion, New York (NY) 1965
Ashby WR (1956) Introduction to Cybernetics, New York (NY) 1956
Axelrod R (1997) The Complexity of Cooperation: Agent-Based Models of Competition and Collaboration, Princeton (NJ) 1997
Axelrod R, Cohen MD (1999) Harnessing Complexity – Organiza-tional Implications of a Scientific Frontier, New York (NY) 1999
Bar-Yam Y (1997) Dynamics of Complex Systems, Reading (MA) 1997
Bellmann K, Mildenberger U (1996) Komplexität und Netzwerke. In: Bellmann K, Hippe A (eds) Management von Unternehmens-netzwerken - Interorganisationale Konzepte und praktische Umset-zung, Wiesbaden 1996, pp 121-156
28
Bengtsson M, Kock S (2000) "Coopetition" in Business Networks - to Cooperate and Compete Simultaneously. In: Industrial Marketing Management 29: 411-426
Berreby D (1996) Between Chaos and Order: What Complexity Theory can Teach Business. In: Strategy & Business, 2 (2): 4-12
Berreby D (1998) Complexity Theory: Fact-Free Science Or Busi-ness Tool?. In: Strategy & Business, 4 (3): pp 40-50
Biedenkopf KH (1994) Komplexität und Kompliziertheit. In: Infor-matik-Spektrum, 17: pp 82-86
Bliss C (2000) Management von Komplexität – ein integrierter, sys-temtheoretischer Ansatz zur Komplexitätsreduktion, Wiesbaden 2000
Boisot M (2000) Is There a Complexity Beyond the Reach of Strat-egy?. In: Emergence – Journal of Complexity Issues in Organiza-tions and Management, 2 (1): pp 114-134
Brandenburger A, Nalebuff B (1996) Co-opetition, New York (NY) 1996
Brandes D (2002) Einfach managen. Klarheit und Verzicht – der Weg zum Wesentlichen, Wien 2002
Bronner R (1992) Komplexität. In: Frese, E. (ed) Handwörterbuch der Organisation, 3rd edn, Stuttgart 1992, c 1121-1130
Brown SL, Eisenhardt KM (1997) The Art of Continous Change: Linking Complexity Theory and Time-paced Evolution in Relent-lessly Shifting Organization. In: Administrative Science Quartely, 42: 1-34
Bruhn M (1997) Hyperwettbewerb: Merkmale, treibende Kräfte und Management einer neuen Wettbewerbsdimension. In: Die Unter-nehmung, 51: 339-357
Campbell DJ (1988) Task Complexity: A Review and Analysis. In: Academy of Management Review, 13: 40-52
Child J (1972) Organizational Structure, Environment and Perform-ance: the Role of Strategic Choice. In: Sociology, 6: 1-22
29
Church M (1999) Organizing simply for complexity: beyond meta-phor towards theory. In: Longe Range Planning, 32: 425-440
Cooksey RW (2001) What Is Complexity Science? A Contextually Grounded Tapestry of Systemic Dynamism, Paradigm Diversity, Theoretical Eclecticism. In: Emergence – Journal of Complexity Is-sues in Organizations and Management, 3 (1): 77-103
Coveney P, Highfield R (1996) Frontiers of Complexity: The Search for Order in a Chaotic World, London 1996
Cowan GA, Pines D, Meltzer D (eds, 1999) Complexity : Meta-phors, Models, and Reality, New York (NY) 1999
D’Aveni R (1994) Hypercompetition. Managing the Dynamics of Strategic Maneuvering, New York (NY) 1994
Dent EB (1999) Complexity Science: a Worldview Shift. In: Emer-gence – Journal of Complexity Issues in Organizations and Man-agement, 1 (4): 5-19
Deser F (1997) Chaos und Ordnung im Unternehmen: Chaosfor-schung als ein Beitrag zum besseren Verständnis von Unternehmen als nichtlinearen dynamischen Systemen, Heidelberg 1997
Dijkum van C (1997) From cybernetics to the science of complexity. In: Kybernetes, 26: 725-737
Dittmer G (2001) Rationales Management. Komplexität methodisch meistern, Berlin et al. 2001
Dooley KJ, van de Ven AH (1999) Explaining Complex Organiza-tional Dynamics. In: Organizational Science, 10: 358-372
Dörner D, Kreuzig HW, Reither F (1994) Lohhausen. Vom Umgang mit Unbestimmtheit und Komplexität. Zwölf Jahre danach: Lohhau-sen im Rückblick, Göttingen 1994
Eisenhardt K, Bhatia MM (2002) Organizational Complexity and Computation. In: Baum JAC (ed) The Blackwell Companion to Or-ganizations, Oxford Malden (MA) 2002, pp 442-466
30
Emery FE, Trist EL (1965) The Causal Texture of Organizational Environments. In: Human Relations, 18: 21-32
Fallgater MJ (1995) Grenzen der Schlankheit. Lean Management braucht Organizational Slack. In: Zeitschrift Führung und Organisa-tion, 64: 215-220
Frizelle G (1998) The Management of Complexity in Manufactur-ing, London 1998
Galbraith J (1973) Designing Complex Organizations, Reading (MA) et al. 1973
Galbraith J (2001) Designing Organizations, 2nd edn, San Francisco (CA) et al. 2001
Galbraith J, Lawler III EE (1993) Organizing for the Future: The New Logic for Managing Complex Organizations, San Francisco (CA) et al. 1993
Galbraith J, Downey D, Kates A (2001) Designing Dynamic Or-ganizations, New York (NY) 2001
Gell-Mann M (1994) The Quark and the Jaguar: Adventures in the Simple and the Complex, New York (NY) 1994
Gharajedaghi J (1999) Systems Thinking: Managing Chaos and Complexity: A Platform for Designing Business Architecture, Bos-ton (MA) et al. 1999
Gilbert X, Strebel PJ (1987) Strategies to outpace the competition. In: Journal of Business Strategy, 8: 28-36
Göbel E (1998) Theorie und Gestaltung der Selbstorganisation, Ber-lin 1998
Gomez P, Probst GJB (eds, 1989) Vernetztes Denken: Unternehmen ganzheitlich führen, Wiesbaden 1989
Gomez P, Probst GJB (1995) Die Praxis des ganzheitlichen Prob-lemlösens: vernetzt denken, unternehmerisch handeln, persönlich überzeugen, Bern et al. 1995
Götze U, Henselmann K, Mikus B (eds, 2001) Risikomanagement, Heidelberg 2001
31
Grint K (1997) Fuzzy Management – Contemporary Ideas and Prac-tices at Work; New York (NY) 1997
Grothe M (1997) Ordnung als betriebswirtschaftliches Phänomen – Die Bedeutung von Koordination und Komplexität; Wiesbaden 1997
Gutenberg E (1965) Grundlagen der Betriebswirtschaftslehre, Band 1 Die Produktion, 10th edn, Berlin Heidelberg New York (NY) et al. 1965
Hagel J (1988) Managing Complexity. In: McKinsey Quarterly, 14 (Spring): 2-23
Hall N (ed, 1991) Exploring Chaos: A Guide to the New Science of Disorder, New York (NY) 1991
Holland JH(1998) Emergence: From Chaos to Order, Cambridge (MA) 1998
Horgan J (1995) From Complexity to Perplexity. In: Scientific A-mercian, 273 (6): 104-109
Hub H (1994) Ganzheitliches Denken im Management: komplexe Aufgaben PC-gestützt lösen, Wiesbaden 1994
Jahns C (2001) Komplexität und Wettbewerb: Herausforderungen für das Management. In: Das Wirtschaftsstudium, 30: 690-694
Jenner T (2000) Hybride Wettbewerbsstrategien in der deutschen Industrie - Bedeutung, Determinanten und Konsequenzen für die Marktbearbeitung. In: Die Betriebswirtschaft, 60: 7-22
Jost HR (2000) Komplexitäts-Fitness – Wandel erfolgreich gestal-ten, Zürich 2000
Kappelhoff P (1999a) Der Netzwerkansatz als konzeptueller Rah-men für eine Theorie interorganisationaler Netzwerke. In: Sydow J, Windeler A (ed) Steuerung von Netzwerken, Opladen 1999, pp 25-57
Kappelhoff P (1999b) Komplexitätstheorie und Steuerung von Netzwerken. In: Sydow J, Windeler A (eds) Steuerung von Netz-werken, Opladen 1999, pp 347-389
32
Kappelhoff P (2001) Komplexitätstheorie: Neues Paradigma für die Managemenforschung? In: http://www.wiwi.uni-wuppertal.de/kappelhoff/papers/kt_management.pdf, [15.6.2002]
Kauffman SA (1993) The Origins of Order, New York (NY) 1993
Kelly K (1998) New Rules for the New Economy: 10 Radical Strategies for a Connected World, Reading (MA) 1998
Kelly S, Allison MA (1998) The Complexity Advantage: How the Science of Complexitiy Can Help Your Business Achieve Peak Per-formance, New York (NY) 1998
Khurana A (1999) Managing complex production processes. In: MIT Sloan Management Review, 1999, Winter, pp 85-97
Köster O (1998) Komplexitätsmanagement in der Industrie. Kun-dennähe und Effizienz in der Leistungserstellung, Wiesbaden 1998
Krapp H, Wägenbauer T (eds, 1997) Komplexität und Selbstorgani-sation. „Chaos“ in den Natur und Kulturwissenschaften, Paderborn 1997
Krickx GA (2000) The Relationship between Uncertainty and Verti-cal Integration. In: The International Journal of Organizational Analysis, 8: 309-329
Krug HE (1992) Diagnose und Design komplexer Organisationen. In: Henning K (ed) Methodik und Praxis der Komplexitätsbewälti-gung, Berlin 1992, pp 63-69
Laux H, Liermann F (1997) Grundlagen der Organisation: Die Steu-erung von Entscheidungen als Grundproblem der Betriebswirt-schaftslehre. 4th edn, Berlin Heidelberg New York (NY) 1997
Lawler EE, Finegold D (2000) Individualizing the Organization: Past, Present, and Future. In: Organizational Dynamics, 29: 1-15
Lewin R (1999) Complexity: Life at the Edge of Chaos, 2nd edn, Chicago (IL) 1999
Lewin R, Parker T, Regine B (1998) Complexity Theory and the Organization: Beyond the Metaphor. In: Complexity, 4 (March/April): 36-40
33
Lewin R, Regine B (2001) Weaving Complexity & Business: En-gaging The Soul at Work, London New York (NY) 2001
Lindberg C, Taylor J (1997) From the Science of Complexity To Leading In Uncertain Times. In: Journal of Innovative Management, 2 (Summer): 22-34
Lissack MR, Gunz H (eds, 1999) Managing complexity in organiza-tions: A view in many directions, Westport (CT) 1999
Lissack MR, Roos J (2000) The Next Common Sense: The e-manager’s guide to mastering complexity, London 2000
Luczak H, Fricker A (1997) Komplexitätsmanagement – ein Mittel der strategischen Unternehmensgestaltung. In: Schuh G, Wiendahl HP (eds) Komplexität und Agilität. Steckt die Produktion in der Sackgasse? Festschrift zum 60. Geburtstag von Professor Walter Eversheim, Berlin Heidelberg New York (NY) 1997, pp 309-323
Luhmann N (1980) Komplexität. In: Grochla E (ed) Handwörter-buch der Organisation, 2nd edn, Stuttgart 1980, c 1064-1070
Magala S (2000) Critical complexities – From marginal paradigms to learning networks. In: Journal of Organizational Change Man-agement, 13: 312-333
Maguire S, McKelvey B (1999) Complexity and Management: Mov-ing From Fad To Firm Foundations. In: Emergence – Journal of Complexity Issues in Organizations and Management, 1 (2): 19-61
Mainzer K, Müller A, Saltzer W (eds, 1998) From Simplicity to Complexity, Part II, München 1998
Malik F (1996) Strategie des Managements komplexer Systeme : ein Beitrag zur Management-Kybernetik evolutionärer Systeme, 5th edn, Bern Stuttgart 1996
Malik F (2000) Systemisches Management, Evolution, Selbstorgani-sation: Grundprobleme, Funktionsmechanismen und Lösungsansätze für komplexe Systeme, 2nd edn, Bern Stuttgart Wien 2000
Maruyama M (1992) Context and Complexity – Cultivating Contex-tual Understanding; New York (NY) 1992
34
Maucher I (2001) Komplexitätsbewältigung durch Entwicklung und Gestaltung von Organisation, München Mering 2001
McAfee A, Oliveau F-X (2002) Confronting the Limits of Networks. In: MIT Sloan Management Review, 43 (Summer): 85-87
McKelvey B (1999) Complexity Theory in Organization Science: Seizing the Promise or Becoming a Fad ?. In: Emergence – Journal of Complexity Issues in Organizations and Management, 1 (1): 5-31
McKergow M (1996) Complexity Science and Management: What´s in it for Business?. In: Long Range Planning, 29: 721-727
Merry U (1995) Coping with Uncertainty: Insights from the New Sciences of Chaos, Self-Organization, and Complexity, Westport (CT) 1995
Miller D (1993) The Architecture of Simplicity. In: Academy of Management Review, 18: 116-138
Mintzberg H (1989) Mintzberg on Management: Inside Our Strange World of Organizations, New York (NY) 1989
Musès C (2000) Simplifying complexitiy – The greatest present challenge to management and government. In: Kybernetes, 29: 612-637
Page SE (1998) Uncertainty, Difficulty and Complexity, Santa Fe Institute Working Paper 98-08-076. In: http://www.santafe.edu/sfi/publications/Working-Papers/98-08-076E.pdf, [15.5.2001]
Pascale RT (1999) Surfing the Edge of Chaos. In: Sloan Manage-ment Review, 40 (Spring): 83-94
Peak D, Frame M (1995) Komplexität – das gezähmte Chaos, Basel, Boston, Berlin 1995
Perrow C (1972) Complex Organizations: A Critical Essay, London 1972
Phelan SE (2001) What is complexity science, really?. In: Emer-gence – Journal of Complexity Issues in Organizations and Man-agement, 3 (1): 120-136
35
Picot A, Reichwald R, Wigand RT (1996) Die grenzenlose Unter-nehmung. Information, Organisation und Management, 2nd edn, Wiesbaden 1996
Picot A, Reichwald R, Wigand RT (2001) Die grenzenlose Unter-nehmung. Information, Organisation und Management, 4th edn, Wiesbaden 2001
Pine BJ (1993) Mass Customization. The New Frontier in Business Competition, Boston (MA) 1993
Price Waterhouse Change Integration Team (eds, 1995) The Paradox Principles: How High-Performance Companies Manage Chaos, Complexity, and Contradiction to Achieve Superior Results, New York (NY) et al. 1995
Prigogine I (1997) The end of certainty, New York (NY) 1997
Proff H, Proff HV (1997): Möglichkeiten und Grenzen hybrider Strategien. In: Die Betriebswirtschaft, 57. Jg., 1997, 6, pp 796-809
Puhl H (1999) Komplexitätsmanagement : ein Konzept zur ganzheit-lichen Erfassung, Planung und Regelung der Komplexität in Unter-nehmensprozessen, Kaiserslautern 1999
Raufeisen M (1997) Komplexitätsreduktion in der Auftragsabwick-lung. In: Zeitschrift für Betriebswirtschaft, 67: 125-149
Regine B, Lewin R (2000) Leading at the Edge: How Leaders Influ-ence Complex Systems. In: Emergence – Journal of Complexity Is-sues in Organizations and Management, 2 (2): 5-24
Reiss M (1992a) Optimale Unternehmenskomplexität: Schlüsselgrö-ße für exzellente Unternehmensführung. In: Personal, 44: 414-418
Reiss M (1992b) Optimieren der Unternehmenskomplexität. In: io Management, 61 (7/8): 25-31
Reiss M (1992c) Rationalisierung als „Tuning“ von Komplexität. In: REFA-Nachrichten, without volume (3): 23-26
Reiss M (1993a) Komplexität beherrschen durch "Orga-Tuning". In: Reiss M et al. (eds) Komplexität meistern - Wettbewerbsfähigkeit sichern, Stuttgart 1993, pp 1-41
36
Reiss M (1993b) Komplexitätsmanagement (I), in Das Wirtschafts-studium, 22: 54-60
Reiss M (1993b) Komplexitätsmanagement (II), in Das Wirtschafts-studium, 22: 132-137
Reiss M (1994a) Optimale Komplexität von Führungsorganisatio-nen. In: Die Unternehmung, 48 (2): 83-97
Reiss M (1994b) Schlanke Matrix. In: Zeitschrift Führung und Or-ganisation, 63 (1): 6-10
Reiss M (1996) Grenzen der grenzenlosen Unternehmung. Perspek-tiven der Implementierung von Netzwerkorganisationen. In: Die Un-ternehmung, 50 (3): 195-206
Reiss M (2000) Unternehmertum in Netzwerken. In: Reiss M (ed) Netzwerk-Unternehmer. Fallstudien netzwerkintegrierter Spin-offs, Ventures, Start-ups und KMU, München 2000, pp 1-37
Reiss M (2001) Netzwerk-Kompetenz. In: Corsten H (ed): Unter-nehmungsnetzwerke. Formen unternehmungsübergreifender Zu-sammenarbeit, München Wien 2001, pp 121-187
Reiss M, Beck TC (1993) Implementierung integrierter Gruppen-konzepte. In: Corsten H, Will T (eds) Lean Production, Stuttgart et al. 1993, pp 107-134
Reiss M, Gassert H, Horváth P (eds, 1993) Komplexität meistern – Wettbewerbsfähigkeit sichern, Stuttgart 1993
Reiss M, Beck TC (1995) Kernkompetenzen in virtuellen Netzwer-ken. Der ideale Strategie-Struktur-Fit für wettbewerbsfähige Wert-schöpfungssysteme ?. In: Corsten H, Will T (eds) Unternehmungs-führung im Wandel. Strategien zur Sicherung des Erfolgspotentials, Stuttgart 1995, pp 33-60
Reiss M, Grimmeisen M (1995) Komplexitätsmangement im Diens-te des Controlling. In: Steinle C, Eggers B, Lawa D (eds) Zukunfts-gerichtetes Controlling, Wiesbaden 1995, pp 40-59
Reiss M, Beck TC (2000) Netzwerkorganisation im Zeichen der Koopkurrenz. In: Foschiani S, Habenicht W, Schmid U, Wäscher G (eds) Strategisches Management im Zeichen von Umbruch und
37
Wandel, Festschrift für Prof. Dr. Erich Zahn zum 60. Geburtstag, Stuttgart 2000, pp 315-340
Reiss M, Koser M (2001) Third Party Customization. In: Blecker T, Gemünden HG (eds) Innovatives Produktions- und Technologiema-nagement, Berlin et al. 2001, pp 55-85
Reiss M, Bernecker T (2003) Entwicklungsdynamik von Dienstleis-tungsnetzwerken. In: Bruhn M, Stauss B (eds) Jahrbuch Dienstleis-tungsmanagement 2003: Dienstleistungs-Netzwerke, Wiesbaden 2003, in print
Reither F (1997) Komplexitätsmanagement- Denken und Handeln in komplexen Situationen; München 1997
Richardson K, Cilliers P (2001) What Is Complexity Science? A View from Different Directions. In: Emergence – Journal of Com-plexity Issues in Organizations and Management, 3 (1): 5-24
Roters M (1988) Komplexität und Dynamik als Einflußgrößen der Effizienz von Organisationen, Frankfurt am Main et al. 1988
Rumelt RP (1974) Structure and Economic Performance, Cambridge (MA) 1974
Rycroft RW, Kash DE (1999) The Complexity Challenge, London 1999
Saeed B, Young D (1998) Managing the Hidden Costs of Complex-ity. In: The Boston Consulting Group (ed) Opportunities for Action, Boston (MA) 1998, pp 1-3
Scherer E, Dobberstein M (1996) Komplexität in der Produktion be-herrschen?. In: io Management, 65 (3): 60-64
Schewe G (1999) Unternehmensstrategie und Organisationsstruktur. In: Die Betriebswirtschaft, 59: 61-75
Schiemenz B (1996) Komplexität von Produktionssystemen. In: Kern W, Schröder HH, Weber J (eds) Handwörterbuch der Produk-tionswirtschaft, 2nd edn, Stuttgart 1996, c 895-904
Schlange LE, Schüller A (eds, 1994) Komplexität und Manage-mentpraxis. Reale Visionen zum Komplexitätsmanagement, Stutt-gart 1994
38
Scholz C (2000) Strategische Organisation. Multiperspektivität und Virtualität, 2nd edn, Landsberg am Lech 2000
Schwenk-Willi U (2000) Integriertes Komplexitätsmanagement : Anleitungen und Methodiken für die produzierende Industrie auf Basis einer typologischen Untersuchung, St. Gallen 2000
Sherman H, Schultz R (1999) Open Boundaries: Creating Business Innovation Through Complexity, Reading (MA) 1999
Stacey RD (2000) Strategic Management and Organisational Dy-namics, London, New York (NY) et al. 2000
Stacey RD (2001) Complex Responsive Processes in Organizations: Learning and Knowledge Creation, London 2001
Stacey RD, Griffin D, Shaw P (2000) Complexity and Management: Fad or Radical Challenge to Systems Thinking?, London New York (NY) 2000
Staehle W (1999) Management: eine verhaltenswissenschaftliche Perspektive, 8th edn, München 1999
Standish RK (2001) On complexity and emergence. In: Complexity International, 9. Jg., 2001, Paper ID: standi09, URL: http://www.csu.edu.au/ci/vol09/standi09/ [3.7.2002]
Stein DL (ed, 1989) Lectures in the sciences of complexity, Red-wood City (CA) et al. 1989
Streufert S, Swezey RW (1986) Complexity, Managers, and Organi-zations, London 1986
Stüttgen M (1999) Strategien der Komplexitätsbewältigung in Un-ternehmen – Ein transdisziplinärer Bezugsrahmen, Stuttgart Bern, Wien 1999
Tasaka H (1999) Twenty-first-century Management and the Com-plexity Paradigm; in: Emergence – Journal of Complexity Issues in Organizations and Management, 1 (4): 115-123
Taylor MC (2001) The Moment of Complexity: Emerging Network Culture, Chicago (IL) 2001
39
Trout J, Rivkin S (1999) The Power of Simplicity- A Management Guide to Cutting Through the Nonsense and Doing Things Right, New York (NY) 1999
Underwood J (2001) Thriving in E-Chaos, Roseville (CA) 2001
Venkatraman N (1989) The Concept of Fit in Strategy Research: Toward Verbal and Statistical Correspondence. In: Academy of Management Review, 14 (3): 423-444
Weick KE, Sutcliffe KM (2001) Managing the Unexpected: Assur-ing High Performance in an Age of Complexity, Chicago (IL) 2001
Westphal JR (2001) Komplexitätsmanagement in der Produktions-logistik. Ein Ansatz zur flussorientierten Gestaltung und Lenkung heterogener Produktionssysteme, Wiesbaden 2001
Wildemann H (2000) Komplexitätsmanagement : Vertrieb, Produk-te, Beschaffung, F&E, Produktion, Administration, München 2000
Wood R (2000) Managing Complexity – How businesses can adapt and prosper in the connected economy, London 2000
Zahn E, Foschiani S (2000) Strategien und Strukturen für den Hy-perwettbewerb. In: Wojda F (ed) Innovative Organisationsformen, Stuttgart 2000, pp 89-113
Zelewski S (1997) Komplexitätstheorie. In: Mertens, P. (ed) Lexi-kon der Wirtschaftsinformatik, 3rd edn, Berlin Heidelberg New York (NY) 1997, pp 230-232
