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The emergence of complex systems theory

The leading thinker behind the study of complexity is Edgar Morin. He dates the notion of complexity to the early 1950’s where the first connections were made between cybernetics, systems theory and information theory. Consequently, the notion of complexity emerged (Morin 1990).

With three theoretical foundations, complexity is still hard to define precisely and concisely. Complexity is certainly not to be interpreted as something that is simply “complicated” but rather as that which is constructed of many related parts. Simon (1962) sidestepped the daunting task of defining complexity by stating that the property of a complex system refers to the large number of parts that interact in a non-simple way. Having studied the evolution of science and systems theory, Checkland (1981) states that complexity is present when there are more variables than one scientist can manage. He also professes that to study such systems, complexity must differentiate itself of the mechanist way of reductionism by attempting to create relationships amongst entities. Likewise in this research, the importance of complex systems will come from its emphasis on the dynamic relationships between the elements more than from the constitution of thr elements themselves.

Characteristics of complex systems

Another great thinker behind the complex approach is Jean-Louis Lemoyne. His complex approach has led to better understanding thought systems. In point and fact, a system has been defined as something identifiable (system) which is in something (environment), for something (project), does something (function), by some thing (structure) which transforms in time (evolution). From this, the four dimensions of systems have been identified: subject, object, project and environment. He then goes on to describe the relationships between the elements of a complex system, there are three founding principles: the dialogical principle, the organizational recursion principal, and the hologrammatical principle (LeMoigne 1995).

The dialogical principle concerns the antagonistic and simultaneously complementary relationships within a system. The paradoxical interrelationship of phenomena creates a balance within the extremes of the system. For example, a system can manage order and disorder at the same time. In fact, the notion of order does not exist without the notion of disorder.

Secondly, the organizational recursion principal allows for the system to regenerate itself, maintain itself, while at the same time influenced by its surrounding environment. This principle is also known as auto-eco-re-organization, where “auto” refers to self-regulation, “eco” refers to its relationship with the environment, and “re” refers to recursive regeneration. For example, the sustainable relationships that make life possible on Earth is self regulated and highly dependent on regenerating itself, with itself, ever since its beginnings.

Lastly, the hologrammatical principle states that the whole is expressed within each part, and inversely, the parts express the whole. Therefore each dimension contains within it all other dimensions. This principle is widely used in the field of mathematics under the name of fractal theory. For example, the capitalist system creates relationships at the macro level between economies much in the same way individual organizations exchange at the micro level.

Now that we have seen the different elements and relationships of complex systems, we can apply that understanding to strongly sustainable business models.

A complex systems approach to shed light on strongly sustainable business models.

Introducing change is difficult to manage. Machiavelli said it best: “There is nothing more difficult to take in hand, more perilous to conduct, or more uncertain in its success, than to take the lead in the introduction of a new order of things” (1515).This risk of initiating change can be studied with a complex system’s approach. Following LeMoigne’s description of complex systems (1995), this approach can describe both the elements of a business model and more importantly the relationships it builds. First we must grasp that the sum of all the elements of a business model make up a business model as a whole system. Second, a business model is a construct of many relationships. We thus reach out to the principles of complex systems to better initiate change and transform the business model.

The widespread publication of Osterwalder and Pigneur’s book “business model generation” (2010) has crystallized which elements can best describe a business model. The authors determined nine building blocks as the parts that make up the whole. They are: Customer Value Proposition, Segments, Customer Relationships, Channels, Key Resources, Key Activities, Partners, Costs and Revenues.

The popularity of this new approach can be explained by the business model canvas. This is a design tool that elegantly places these nine building blocks on a single page. The canvas creates a sense of wholeness to fully understand the business model and more importantly, graphically demonstrate the relationships between the different elements.

Some criticism has surfaced regarding the condensed description of a business model given by Osterwalder. This canvas comes from an innovation bias and doesn’t account for other aspects of managing an organization, such as corporate structure, business objectives, performance measurements, strategy management and competition analysis (Rosenberg et al., 2011). To that list, we would add corporate social and environmental responsibility in creating multiple forms of value. Nonetheless, this approach to understanding and describing a business model allows to focus on the relationships between the elements. This systemic perspective has proven useful in ensuing research to include a strongly sustainable aspect to business models as illustrated below.

 Complex_systems_1

Figure 5. Joyce (2013)

A yet to be published dissertation by Upward (2013) addresses the need for clearly defining the concept of strongly sustainable business models and the need for tools to initiate practical action. His ontological goals combined with a canvas tool are augmented by a systems approach. He critiques the profit centred aspect of Osterwalder’s business model canvas and constructs his canvas to help distinguish between strong and weak sustainability.

Sustainability is about insuring the right relationships (Brown & Garver 2009). LeMoigne described three relationships that make up complex systems which can be applied to strongly sustainable business models. There are many examples of the dialogical principle embedded in a business model: exploitation vs exploration, internal activities vs external sourcing and value creation vs cost implication. Therefore a business model manages antagonistic yet complimentary relationships. A strongly sustainable business models manage these opposing forces as a coherent and harmonious system.

The second characteristic complex systems is the recursive capacity where the presence of feedback loops that allow the system to evolve. For Friedman (1970) the social responsibility of a firm is to create profits. On the other hand, as Drucker (1954) elegantly stated, the purpose of “a business is to create a customer”. As both actions are dynamic in a business model, this creates a feedback loop. The more an organization creates customers, the more it generates profits. The more profits it generates, the better it can create customers. This emphasize the critique of sustainable development that if simply using profits as a notion of success, then society will not be able to provide for itself. Therefore a strongly sustainable business model includes a double feedback loops that insures the well-being of the organization itself, and the well-being of the greater system, in case societal development.

The hologramatical principle of complex systems is less discernible to describe strongly sustainable business models. It entails that the structure and relationships developed at the business model level can be found within the parts as well as within the greater macro-economic system. This means that if we were to take a look at the micro scale of a business model it should have the right relationships that generate positive benefits for all stakeholders. At the macro scale, a 21st century form of capitalism must sustainably answer the needs of society and its future generations.

 In the end, a complex systems approach ensures a broader and dynamic view when studying the relationships in a business model. This is important in our research, because qualifying a business model as strongly sustainable can only be done with an understanding of the relationships described by complex systems. As suggested by Brown and Garver (2009), the means to reaching sustainability is by creating the right relationships at all levels of the system. This is why we use a complex systems approach to make sure the relationships between the organization, the environment and society are strongly sustainable.

Now that we have shown how a business model manifests all the characteristics of a complex system, we will look further into how a business model innovation can be understood as a practice.

6 thoughts on “Complex systems approach for business models

  1. Where is the bibiography?

    More specifically, I would be interested to better understand the following assertion:
    “This canvas comes from an innovation bias and doesn’t account for other aspects of managing an organization, such as corporate structure, business objectives, performance measurements, strategy management and competition analysis (Rosenberg et al., 2011).”

    What is:
    (Rosenberg et al., 2011) ??

    Thanks

  2. Thank you very much.
    Indeed, I searched for this information for quite a long time without success before asking you.

    Thierry
    PS: for your information, I was looking for this information in relation to a new research project
    CBOD (Cloud-Based Organizartional Design)
    http://www.ritm.u-psud.fr/2014/01/le-projet-cbod-selectionne-par-lanr/ for which we are exploring methods for helping the decision making for organizational design in the context of Cloud Computing systems, and in particular how we can model and simulate such systems (Ontologies? System Dynamics, Agent-based simulation).

  3. Cloud computing represents a context in which new (complex) business models have to be defined. This context (business ecosystem?) may impact the organizations both internally and externaly, involve a variety of actors, and transform the value chain.
    One of the issue is really to understand the functioning of this context, and finding the adequate models that can be used to help our understanding (from a multi disciplinary point of view). Ontology modeling (cf. Alexosterwalder) may be useful, but indeed not able to take into account the feeback loop that may exist, and complex chain of causality. This is why I was interested about your reference pointing the limitations of “ontology” approaches.

    Many researches area now looking at this context of Cloud computing, and more generally at business models in the digital words.
    Here are a couple of references I have found useful such as:
    Giessmann, A., Fritz, A., Caton, S., & Legner, C. (2013). A method for simulating cloud business models: a case study on platform as a service. In ECIS 2013 (pp. 1–12).
    moby – methodology for business dynamics http://moby.iao.fraunhofer.de/
    (actually I have now an extended bibliography of “business model” research on the cloud, but nothing very surprising)

    Having said that, I am still looking at identifying “system dynamic” & “agent-based modeling” approaches that have been used to model digital business ecosystems, and see to which extend it can be applied to the context of the cloud.

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