The unpredictable nature of online social communities and competitive business environments

During my last semester as an MBA student at UT-Austin, I took a class called Managing Complexity. I didn’t know it at the time, but this class would be one of the best (if not thee best) class I would take at McCombs. I was enlightened to new approaches in which to view/manage people-based organizations (business or otherwise) and these learnings would resonate on a daily basis during my my professional career. Managing Complexity was all about the study of complex adaptive systems (CAS) and the implications of CAS when attempting to manage complex business environments. For those who have not heard the term “complex adaptive system”, a little background…

The study of CAS can get pretty dense and one could devote an entire career researching the topic. I am by no means one of these people but names like Paul Cilliers, Lynda Gratton and Reuben McDaniel (my professor) come to mind. At a basic level, a complex adaptive system is a system that is made up of agents (e.g. humans) capable of consuming, processing and reacting to input, and potentially changing their behavior based on this input (e.g. husband/wife relationship, surgical team, business environment, financial system, basketball team, etc.). These interactions between agents are non-linear meaning that a particular input will not necessarily have the same effect on the agents (and system) if experienced during different points in time. For example, a sarcastic comment to my wife may have varying effects on our relationship based on her mood which is a result of other interactions/influences throughout the day. These systems are products of the interactions between the agents and therefore the system landscape is constantly evolving and changing state. Furthermore, historical data cannot be used to accurately predict how a CAS will behave based on a particular set of inputs. Take the current financial crisis as an example of this. Based on historical information and financial modeling, the mortgage backed security crisis should have never happened. The golden nugget here is that complex adaptive systems are unpredictable, which is to say that business environments are inherently unpredictable. This is unlike a mechanistic system (e.g. computer, car, TV, etc.) where a particular input received within a particular situation will render a predictable result. So, it turns out that the age old metaphor that equates a business to a “well oiled machine” is a poor analogy. The following is a summary of some key aspects of complex adaptive systems in the context of business environments:

  • Agents – A company, its customers and its competitors can all be considered agents within a business environment. As these agents interact with each other, their behavior changes and interactions evolve. These interactions have a profound effect on the future business landscape.
  • Non-linear interactions – Interactions between agents (e.g. customers, competitors, company reps, etc.) will cause varying results within the system depending on other qualities of the system at that time.
  • Unpredictability – Due to the non-linear interactions, these systems are inherently unpredictable. Contrary to popular (yet uninformed) thought, the behavior of these systems cannot be accurately modeled like mechanistic systems.
  • Self-organization – These systems will self-organize and agents will gravitate towards areas of the system that reflect their behavior with respect to the environmental factors in play. For instance, the operating system business landscape has self-organized into the three interest groups; business professional group (Microsoft Windows), trendy and artistic group (Apple’s Mac), and righteousness of open-source group (Linux).
  • Emergence – Complex adaptive systems have emergent qualities resulting from characteristics that evolve based on agent interactions (e.g. Apple’s brand buzz is an emergent property based on many factors including word of mouth, pop culture, opinions of thought leaders, etc.). The direction of this emergence cannot be accurately predicted.
  • Co-evolution – As agent behavior changes and evolves within a business environment, so does the business environment itself since the environment is a product of the agent interactions and behaviors.

At the same time I was taking this class I was spending quite a bit of time researching social media and the ways in which social media can help businesses obtain a competitive advantage. I started to realize that, similar to business environments, online social communities are complex adaptive systems as well. For instance, online social communities have agents (members) who have interactions with one another which can result in self-organization (sub-groups, expertise hierarchies, etc.), emergent community qualities and (as many online community managers can attest to) unpredictable results. The irony here is that we’re using one CAS (online social community) to help manage the complexities in another CAS (business environment). You’re probably asking yourself “so what”? Well, the “so what” is that understanding the properties of complex adaptive systems can be a monumental advantage in understanding how online social communities can help businesses from a marketing perspective and an equal advantage in understanding how to set up and run an successful online community.

To summarize, the properties of complex adaptive systems can be used to explain:

  1. reasons why online social communities are such an effective marketing tool, and
  2. strategies that should be employed for creating and managing successful online social communities

In my next few posts, I’ll offer up thoughts for both of these areas. In the meantime, please feel free to chime in with your thoughts!

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