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This article explores how complexity theory can help marketers to understand a market and to operate within it. Essentially, it argues that complexity theory has the potential to provide both global and some local explanations of markets and is complementary to local theories like relationship marketing that may be more familiar to marketing managers. It establishes four types of complex systems that might be used to model social systems. Of these four types, complex adaptive systems seem most appropriate to describe markets. This is illustrated in an investigation of Honda in the global automobile industry. Implications for marketing managers centre on the need to understand feedback loops at many levels of a path‐dependent system that are inherently difficult to predict and control.

This article proposes an adaptive strategy for managing knowledge in complex organizations. Specifically, this article aims to extend understanding in the field of knowledge management (KM) by examining how an adaptive strategy for managing knowledge can help organizations become innovative and build dynamic capabilities.

Literature on complexity theory and KM is reviewed to propose the development of an adaptive strategy that will assist organization in managing knowledge and becoming innovative. The paper is structured around the following constructs: complexity theory, complex adaptive systems, and KM.

A link between an adaptive strategy for managing knowledge, innovation and dynamic capability is established. The central proposition of the article is the organizations that follow adaptive complex processes for managing knowledge are better able to compete in the market today.

This article extends prior research on KM by proposing complexity theory as a framework for establishing adaptive strategies for managing knowledge and fostering innovation.

With the dramatic environmental changes and fierce competition that organizations are faced with today, managing knowledge becomes critical for driving creativity and adapting to changing markets. Organizations lack direction on how best to develop an adaptive strategy for managing knowledge. The revelation of adaptive processes for managing knowledge in complex systems can lead to more effective KM practices and a higher rate of creativity and flexibility.

The study answers recent calls for defining processes for the second generation of KM that shift focus from the codification and transfer of knowledge to the creation of new knowledge. Although previous studies have established a link between complex adaptive systems and KM, this study takes it one step further in defining an integrative strategy for the creation of knowledge based on the processes of complex adaptive systems. The paper provides a foundation for future studies to test the causal relationship between adaptive processes for knowledge creation and innovation.

The purpose of this paper is to investigate different cybernetic structures of simple adaptive systems and their cognitive and behavioral options.

Using a functional approach, two basic forms of adaptive systems are constructed, which process data on one level respectively two hierarchical levels. Based on that complex combinations of such one‐level and hierarchical structures are investigated.

It is shown how different cybernetic structures enable simple forms of adaptive behavior. A basic blueprint for the controller structure of animal species is derived from them, with a simple “brain” and a unit for “motion control” as subsystems. Four paths of evolutionary growth are identified that allow a widely independent development of these subsystems.

The paper provides a typology of simple adaptive systems and discusses the forms of behavior they can develop with preprogrammed – i.e. evolutionary given or technically programmed – decision‐rules. It discusses the requirements that these decision‐rules can form models enabling adaptive behavior. It is suggested that these requirements hold for the models of more complex adaptive systems, too.

This paper is the first in a series of three on a cybernetic theory distinguishing systems able of preprogrammed adaptation, system‐specific adaptation, and learning.

The purpose of this paper is to develop a conceptual framework for extending an understanding of resilience in complex adaptive system (CAS) such as supply chains using the adaptive cycle framework. The adaptive cycle framework may help explain change and the long term dynamics and resilience in supply chain networks. Adaptive cycles assume that dynamic systems such as supply chain networks go through stages of growth, development, collapse and reorientation. Adaptive cycles suggest that the resilience of a complex adaptive system such as supply chains are not fixed but expand and contract over time and resilience requires such systems to navigate each of the cycles’ four stages successfully.

This research uses the adaptive cycle framework to explain supply chain resilience (SCRES). It explores the phases of the adaptive cycle, its pathologies and key properties and links these to competences and behaviors that are important for system and SCRES. The study develops a conceptual framework linking adaptive cycles to SCRES. The goal is to extend dynamic theories of SCRES by borrowing from the adaptive cycle framework. We review the literature on the adaptive cycle framework, its properties and link these to SCRES.

The key insight is that the adaptive cycle concept can broaden our understanding of SCRES beyond focal scales, including cross-scale resilience. As a framework, the adaptive cycle can explain the mechanisms that support or prevent resilience in supply chains. Adaptive cycles may also give us new insights into the sort of competences required to avoid stagnation, promote system renewal as resilience expands and contracts over time.

The adaptive cycle may move our discussion of resilience beyond engineering and ecological resilience to include evolutionary resilience. While the first two presently dominates our theorizing on SCRES, evolutionary resilience may be more insightful than both are. Adaptive cycles capture the idea of change, adaptation and transformation and allow us to explore cross-scale resilience.

Knowing how to prepare for and overcoming key pathologies associated with each stage of the adaptive cycle can broaden our repertoire of strategies for managing SCRES across time. Human agency is important for preventing systems from crossing critical thresholds into imminent collapse. More importantly, disruptions may present an opportunity for innovation and renewal for building more resilience supply chains.

This research is one of the few studies that have applied the adaptive cycle concept to SCRES and extends our understanding of the dynamic structure of SCRES

The purpose of this paper is to outline an empirical study of how professionals experience work and learning in complex adaptive organisations. The study uses a complex adaptive systems approach, which forms the basis of a specifically developed conceptual framework for explaining professionals’ experiences of work and learning.

Semi-structured interviews were conducted with 14 professionals from a variety of organisations, industry sectors and occupations in Sydney, Australia. The transcripts were subjected to an adapted phenomenographic analysis, and an analysis using the complex adaptive organisations conceptual framework (CAOCF).

The findings indicated that professionals experienced learning mainly through work, where work was experienced as fluid and influenced by varying degrees of emergence, agency, complex social networks and adaptation. Further, the greater the degree of work fluidity, the greater the impetus towards learning through work, empirically indicating that the experience of learning in contemporary organisations is entwined with work.

This study used the concept of complex adaptive organisations as a conceptual framework, coupled with an adapted phenomenographic methodology, to investigate individual professionals’ experiences of work and learning. The adoption of the concept of complex adaptive organisations provided a rigorous way to adopt a complexity approach. In particular, the concept of emergence provides insights into how organisational complexity influences work and, subsequently, learning and adaptation.

The purpose of this paper is to investigate necessary cybernetic structures that allow complex adaptive systems to develop system‐specific behavior.

Following Holland's concept of “adaptive agents”, it is argued that the development of system‐specific forms of goal‐oriented behavior requires a decision to deviate from some default behavior and to trigger any new one, and a mechanism to evaluate the goal‐orientation of this new behavior. Using a functional approach cybernetic structures are developed that are able to carry out these two tasks. Then these structures are added as subsystems to the structure of a simple one‐level adaptive system.

The paper finds that a hierarchical adaptive system can recognize with a higher level controller, if lower level decisions lead to an insufficient degree of goal‐approximation and can use preprogrammed higher level decisions to intervene on the lower level to trigger new system‐specific actions. An additional controller can evaluate the “success” achieved with these new actions and can select the “best” actions found, i.e. the behavior leading to the highest degree of goal‐approximation.

The paper shows necessary cybernetic structures that are seen as core of all complex adaptive systems able to develop system‐specific behavior. It is suggested that the underlying basic concept of “success” understood as a degree of goal‐approximation holds for any adaptive, learning or otherwise improving endeavor.

The paper is the second in a series of three on a cybernetic theory distinguishing system capable of preprogrammed adaptation, system‐specific adaptation, and learning. It shows necessary cybernetic structures that a system can develop individual actions.

We discuss the impact of complexity science on the design and management of health care organizations over the past decade. We provide an overview of complexity science issues and their impact on thinking about health care systems, particularly with the rising importance of information systems. We also present a complexity science perspective on current issues in today’s health care organizations and suggest ways that this perspective might help in approaching these issues.

We review selected research, focusing on work in which we participated, to identify specific examples of applications of complexity science. We then take a look at information systems in health care organizations from a complexity viewpoint.

Complexity science is a fundamentally different way of understanding nature and has influenced the thinking of scholars and practitioners as they have attempted to understand health care organizations. Many scholars study health care organizations as complex adaptive systems and through this perspective develop new management strategies. Most important, perhaps, is the understanding that attention to relationships and interdependencies is critical for developing effective management strategies.

Increased understanding of complexity science can enhance the ability of researchers and practitioners to develop new ways of understanding and improving health care organizations.

This analysis opens new vistas for scholars and practitioners attempting to understand health care organizations as complex adaptive systems. The analysis holds value for those already familiar with this approach as well as those who may not be as familiar.

Complex systems adapt to survive, but little comparative literature exists on various approaches. Adaptive complex systems are generic, this referring to propositions concerning their bounded instability, adaptability and viability. Two classes of adaptive complex system theories exist: hard and soft. Hard complexity theories include Complex Adaptive Systems (CAS) and Viability Theory, and softer theories, which we refer to as Viable Systems Theories (VSTs), that include Management Cybernetics at one extreme and Humanism at the other. This paper has a dual purpose distributed across two parts. In Part 1, the purpose of this paper is to identify the conditions for the complementarity of the two classes of theory. In Part 2, the purpose is to explore (in part using Agency Theory) the two classes of theory and their proposed complexity continuum.

A detailed analysis of the literature permits a distinction between hard and softer approaches towards modelling complex social systems. Hard theories are human-incommensurable, while soft ones are human-commensurable, therefore more closely related to the human condition. The characteristics that differentiate between hard and soft approaches are identified.

Hard theories are more restrictive than the softer theories. The latter can embrace degrees of “softness” and it is explained how hard and soft approaches can be mixed, sometimes creating Harmony.

There are very few explorations of the relationship between hard and soft approaches to complexity theory, and even fewer that draw in the notion of harmony.

Smallholder value chains are dynamic, changing over time in sudden, unpredictable ways as they adapt to shocks. Understanding these dynamics and adaptation is essential for these chains to remain competitive in turbulent markets. Many guides to value chain development, though they focus welcome attention on snapshots of current structure and performance, pay limited attention to the dynamic forces affecting these chains or to adaptation. The paper aims to discuss these issues.

This paper develops an expanded conceptual framework to understand value chain performance based on the theory of complex adaptive systems. The framework combines seven common properties of complex systems: time, uncertainty, sensitivity to initial conditions, endogenous shocks, sudden change, interacting agents and adaptation.

The authors outline how the framework can be used to ask new research questions and analyze case studies in order to improve our understanding of the development of smallholder value chains and their capacity for adaptation.

The framework highlights the need for greater attention to value chain dynamics.

The framework offers a new perspective on the dynamics of smallholder value chains.

This chapter uses the theory of complex systems as a conceptual lens through which to compare the work of Friedrich Hayek with that of Vincent and Elinor Ostrom. It is well known that, from the 1950s onwards, Hayek conceptualised the market as a complex adaptive system. It is argued in this chapter that, while the Ostroms began explicitly to describe polycentric systems as a class of complex adaptive system from the mid-to-late 1990s onwards, they had in fact developed an account of polycentricity as displaying most if not all of the hallmarks of organised complexity long before that time. The Ostromian and Hayekian approaches can thus be seen to share a good deal in common, with both portraying important aspects of society – the market economy in the case of Hayek, and public economies, legal and political systems, and environment resources in the case of the Ostroms – as complex rather than simple systems. Aside from helping to bring out this aspect of the Ostroms’ work, using the theory of complex systems as a framework for comparing the Hayekian and Ostromian approaches serves two other purposes. First, it can be used to show how one widely criticised aspect of Hayek’s theory of society as a complex system, namely his account of cultural evolution via group selection, can be strengthened by an appeal to the work of Elinor Ostrom. Second, it also helps to resolve a tension – ultimately acknowledged by the Ostroms themselves – between some of their explicit methodological pronouncements and the actual, substantive approach they adopted in their analysis of polycentric systems.