Ecosystems Thinking for Social Change

Ecosystem, or “system” thinking is not necessarily about ecology, but uses an ecological metaphor to explore the interconnectedness of various aspects of any system (Mars, Bronstein, & Lusch, 2014).  This is a critical skill for business organizations to aid strategy and innovation.  It is also an area where versatilists often shine, because versatilists are uniquely adept at taking deep knowledge from one system and applying it to their understanding of new systems, often leading to unique insights.  However, that is not what this blog is about.  This is about how a lack of systems thinking is trapping our society into repeating the same issues repeatedly.  This is about how electing people who comprehend systems thinking might be a better means of bringing about social change.

The Heart of Systems Thinking

At the heart of systems thinking is to keep in mind that no problem, no solution, no individual exists in a vacuum.  The whole world is a set of interrelated systems that influence and affect those around it; changes in one system ripple throughout our entire society.  Systems thinking involves attempting to understand and evaluate any problem or solution within the context of the bigger picture.

For instance, take constraint theory (Tulasi & Rao, 2012).  Constraint theory suggests that any system or process is constrained by the least capable or least efficient step in the system; this is often equated with the “weakest link” idea that a chain is only as strong as the weakest link in the chain.  The idea behind constraint theory, however, suggests that if you fortify the weakest link (solving that problem), you have simultaneously created a new “weakest link” (formerly, the next-to-weakest link).  In addition, the newer, stronger link may also have other unintended consequences (maybe by making it stronger, you have also made it bigger, which affects some other function).  In essence, the process to creating a stronger chain is a never-ending task as each solution has ramifications.

In systems thinking, you must evaluate how a solution to one problem may create a new problem or change the dynamics of another system.  This potential new problem must also be evaluated to determine if it is a bigger problem than the one you are attempting to solve, or makes the solution you have proposed untenable.  Problem solving, like creating a stronger chain, is a never-ending process.  However, the intended result is improving the overall whole, ensuring one solution doesn’t create a bigger problem somewhere else.

Unfortunately, without systems thinking, we are failing to create an overall better society, but are remaining mostly stationary.  Solutions examined and evaluated within a vacuum, create ripples that, instead of moving us forward, keep us in a constant state.

Examples of Non-Systems Thinking Challenges

The worst part of failing to apply systems thinking to the problems of our society, is when the same groups of people argue for two independent solutions, which are counterproductive; i.e., when the same group argues for one solution that aggravates another problem they are trying to solve and vice-versa.  It is important to understand that, in and of themselves, the proposed solutions may be perfectly good solutions; it is only when you combine the system effects that issue become apparent.  It is also important to note that this is not an analysis of the merits of any particular solution or point of view.  There is no intent to endorse or oppose any of the individual solutions, simply to illustrate the systems effects of those solutions.

Immigration Reform and Free Trade Agreements

By itself, building a border wall, while economically questionable, is a perfectly legitimate solution to preventing illegal immigration via our southern US borer.  It is not the only solution of course, but it is a possible solution.  We can debate this one way or the other, but even opponents must admit that it is a solution whether they agree it is the right one or not.

Similarly, eliminating or significantly reducing free trade, particularly with low-cost labor countries like Mexico is a legitimate solution to reducing job-loss in the US via off-shore outsourcing by US companies and keeping US investment in the US.  Again, not the only solution, but certainly one way to address the issue.  We can once again debate this, but it must be accepted that it is a solution.

However, when put together, these solutions are counter-productive.  By eliminating the ability for Mexico to continue to develop and build their economic capability (by granting them easy access to US markets and US investment), their standard of living will likely decline.  A decline in standard of living (loss of jobs and the income from it) only perpetuates the growth of illicit enterprises (e.g. drugs) as well as makes illegally emigrating to the US more attractive.  As such, from a systems perspective, reversing free trade agreements will likely compound the issue of illegal immigration, as well as drug smuggling and other issues.  This places even bigger demands on the needs of border protection and immigration control.  These are misaligned solutions from a systems perspective.

Welfare Reform and Birth Control

Another counterproductive combination of arguments is simultaneously arguing for reducing the US welfare system, while simultaneously arguing to eliminate birth control options, including sex education and access to safe abortions.  Again, in and of their own, each of these arguments are perfectly reasonable and can be understood.   Without applying personal judgements on them, they are reasonable goals and can be respected.  From a systems perspective however, these are not isolated issues or goals, they have complex interactions which makes arguing for both less reasonable.   The only logical result of limiting sex education and access to birth control measures is an increase in women and children within the welfare system.  It is illogical to argue for both actions, even if either one of them in isolation can at least be recognized as reasonable.

Cyber Security and Encryption Strength

Just this last week, there were two articles published.  The first one detailed how Russian hackers have been targeting the personal (non-government) cell phones of NATO soldiers to track, intimidate, and spy on them.  The second one detailed the Department of Justice (DoJ) pushing for US technology firms to make it easier for law enforcement to access the encrypted (personal) devices of accused criminals.  Again, arguing for improved safety and security of our personal information, particularly through strong encryption is a reasonable solution to rampant cyber crime.  It is also reasonable to argue that law enforcement should be able to access the information they need to convict criminals for breaking the law.  Unfortunately, you cannot reasonably argue for both as the one comes at the cost of the other.  Arguing that we need to better protect our personal information from thieves, while simultaneously arguing to hobble encryption for government access are mutually exclusive goals.

Understanding the Bigger Picture is Essential

Systems thinking requires us to look at the proposed solutions and understand their ultimate effects.  It asks us to better understand how seemingly separate systems interact and how changes in one creates ripple effects in others.  Besides allowing us to mediate between counter-productive arguments, systems thinking also provides an opportunity to discover new solutions.

By broadening our thinking, systems thinking allows us to uncover new solutions to old problems.  If we see how changes in one system can ripple into others, we can harness these ripples for positive change in our society.  It asks us to look at why things happen, at root causes, rather than addressing the ramifications or symptoms of those problems.  It allows us to explore how numerous problems in our society may be linked by ripple effects of similar issues we haven’t imagined.  For instance, could the rising costs of US health care (and its effects on treatment of mental health issues) be a progenitor of the rising threats of violence and recruitment of disaffected youth by terrorist organizations?   Could the antiquated US tax system be a progenitor of immigration challenges, job-loss through outsourcing, and increased income divisions?  Could US foreign policy be a bigger source of terrorist threats than religious extremism?  Systems thinking helps us see how solving one challenge may also have positive benefits on others.

Unfortunately, we do not look at problems as components in a unified system of systems, we tend to look at individual problems and argue solutions without thinking about the ramifications of those arguments.  We frequently miss the forest for the trees.  The effect is to leave us in a perpetual state of uncertainty, never moving society fully forward no matter how many problems we try to solve.  We never address the true source of the problem, only applying patches that don’t align and don’t solve the underlying problem.  We would all do better if we took a more holistic view of the problems we face, rather than reactively addressing symptoms.



Mars, M., Bronstein, J., & Lusch, R. (2014). Organizations as ecosystems: Probing the value of a metaphor. Rotman Management, 73–77.

Tulasi, C. L., & Rao, A. R. (2012). Review on theory of constraints. International Journal of Advances in Engineering & Technology, 3(1), 334–344. http://doi.org/10.2307/25148735


4 Steps to Initiating Business Model Innovation

Business model innovation is an increasingly common topic.  The advent of freemium and non-linear revenue generation is shaking the foundations of many established organizations whose tried-and-true business models are feeling constrained and sluggish. Even large organizations with strong track records of innovation are feeling consistent pressure to keep up with these rapidly changing market dynamics (Terlep, 2017).  Ironically, many business leaders and strategist have given very little thought to their business model.  Here are four steps, and resources, to assist in getting a grip on initiating business model innovation.

#1 Understand What Your (a) Business Model Is

Perhaps it is an artifact of our technology driven culture where massive companies are built without ever even having a business model, but before you can innovate a business model, you need to understand what a business model is in its most basic form, and what your specific model is.  At its core a business model comprises two basic concepts:

  1. Value Creation (how do you create value); and,
  2. Value Capture (how do you capture some of that value as profit) (Matzler, Bailom, von den Eichen, & Kohler, 2013).

While perhaps pedantic, understanding this basic construct of a business model is highly informative and the progenitor to all other business decisions. They are also the starting place for business model innovation.  Increasing the perceived value of your product without changing the cost to produce it can increase market penetration by being seen as a value offering if you don’t increase price, can increase profit if you capture this new value as profit by also raising prices, or both if the increase in perceived value is greater than the increase in price.  Conversely, maintaining perceived value, but reducing price or the costs to create the product can affect profit.  All business decisions start at this basic level and the business model informs every other aspect of the business.

The part of a business model most misunderstood is the effect of perceived customer value; yet this is one of the most important factors.  Even if your product is no different from competitors, if customers perceive it as more valuable, you have created more value – value you can capture either as market share or in direct profit.  This makes understanding your product’s value proposition a key part of business model innovation.

#2 Understand Your Product/Service Value Proposition

Many businesses either don’t understand the true job that their product performs for their customers, or believe it has no effect on the bottom line (Christensen, Anthony, Berstell, & Nitterhouse, 2007).  Yet, understanding how and why a customer is using your product or service is a key component to evaluating the perceived value.  If you don’t know what job your product is performing, it is difficult to properly affect the perception of how well it does that job.  Christensen, Anthony, Berstell, and Nitterhouse (2007) document several examples of how understanding the right job for your product is essential to understanding perceived value.

For example, how does the value of certain features of your product change depending on how the user is using it?  Does a soccer mom have a different perception of her vehicle than a travelling salesperson?  Does this perceptual difference affect the perceived value of your current product?  Understanding how your products and services are perceived and what their true function is in the lives of its consumers is critical to your business model.  Not only will it help you find ways of maximizing your current business model, it may also lead to new ways of capturing and/or creating value (Bettencourt & Ulwick, 2008).

Understanding how the percieved value of your offering differs, and what contexts affect this perception is a gateway to finding new ways of value capture.  Instead of just offering a product, there may be services when combined with the product could greatly elevate the value.  The easiest example of this is the iTunes store added to the iPod.  Not only did the iTunes store add a significant new revenue stream to apple, it greatly increased the value of the iPod because it worked seamlessly with iTunes to make finding and exploring new music simple.  Capturing “out-of-band” value is an excercise in nonlinearity.

#3 Understand Nonlinearity

Linear bias, the tendency for humans to think in straight-line correlations, can lead to very costly mistakes (Bart de LangheStefano PuntoniRichard Larrick, 2017).  While understanding we live in a nonlinear world is important in and of itself, it can have pronounced effects on business models.  This linear thinking process can severely limit your ability to understand or comprehend business model innovation; we are taught from day one in business school that profit is generate through the difference between the cost to create a product and the price we sell the product for.  In Competing Against Free (2011), Bryce, Dyer and Hatch examine how this linear approach to business models can prevent successful organizations from competing with start-ups using nonlinear business models to capture value indirectly.

The reality is we generate profit based on the cost to create value, and how much of that value we can capture.  This does not suggest that we must capture value directly from the sale of the product, nor does it say we can’t. This is most readily visible in software subscriptions (Microsoft, Adobe) and other technology sectors, but companies like Gillette, HP, and others have built their business similarly for decades.  By selling their “products” at, or below, cost and captured value through the supplies necessary to keep those products functioning they have moved from “product” companies to “service” companies by realigning how they create and capture value.    Evaluating the cost and benefits of these approaches is why understanding nonlinearity is especially important to business model innovation.

#4 Understand Whether to Innovate, How, and When

One of the lessons of Competing Against Free (Bryce et al., 2011) is just because someone else has a different business model, doesn’t necessarily mean you should change yours as well.  Business model innovation is not a simple process and can depend on the conditions of the market and internal dynamics like the presence of leadership capable of making tough decisions and building organizational consensus (Giesen, Riddleberger, Christner, & Bell, 2010).   This is not a process for the faint of heart as it likely affects every aspect of your business from how you market and sell your products, the partner organizations you work with, and even basic accounting, cost-controls, and financial reporting.  Since the business model is the most basic statement of your “theory of business”, changing your model changes everything.

The positive side is that even if you don’t implement a complete business model change, going through these steps will surely uncover a host of ideas about how you can elevate customer perceived value or better capture the value you already create.  It may also help you better understand what new competitive business models might be lurking out there, how they operate, and how you can address them should they come knocking.  Lastly, it may lead to a business model refresh, softening the organization for the day a full business model innovation needs to take place.


Bart de LangheStefano PuntoniRichard Larrick. (2017). Linear Thinking in a Nonlinear World. Harvard Business Review, (June). Retrieved from http://hbr.org/

Bettencourt, L. A., & Ulwick, A. W. (2008). The customer-centered innovation map. Harvard Business Review, 86(5), 109–114. http://doi.org/Article

Bryce, D. J., Dyer, J. H., & Hatch, N. W. (2011). Competing against free. Harvard Business Review, 89(6). Retrieved from https://hbr.org/

Christensen, C. M., Anthony, S. D., Berstell, G., & Nitterhouse, D. (2007). Finding the right job for your product. MIT Sloan Management Review, 48(3), 38. Retrieved from http://sloanreview.mit.edu/

Giesen, E., Riddleberger, E., Christner, R., & Bell, R. (2010). When and how to innovate your business model. Strategy & Leadership, 38(4), 17–26. http://doi.org/10.1108/10878571011059700

Matzler, K., Bailom, F., von den Eichen, S. F., & Kohler, T. (2013). Business model innovation: Coffee triumphs for Nespresso. The Journal of Business Strategy, 34(2), 30–37. http://doi.org/10.1108/02756661311310431

Terlep, S. (2017). Procter & Gamble vs. Nelson Peltz: A Battle for the Future of Big Brands – WSJ. Retrieved October 9, 2017, from https://www.wsj.com/articles/p-g-vs-nelson-peltz-a-battle-over-the-future-of-big-brands-1507485229


The Processes of Innovation

Successful innovation requires more than just an idea, but the knowledge necessary to develop and deliver innovation based on those ideas.  Even with access to rich knowledge resources, failure is an inherent part of the innovation process (McGrath, 2011).  Processes for selecting, promoting, and executing innovative ideas are critical to innovative strategy.

Different Types of Knowledge Development Processes

Capturing the full value of an organization’s knowledge resources requires understanding the value of various knowledge sources and the processes for selecting, promoting, and executing on the most promising ideas.  Not all knowledge has the same value to the organization, nor can it be captured using the same processes (Mahroeian & Forozia, 2012; Wilson & Doz, 2011).  Wilson and Doz identified three different types of knowledge: existential, embedded, and explicit; although, Mahroeian and Forozia simply define knowledge as existing on a continuum between explicit and existential (or tacit). Each of these knowledge resources requires unique processes and systems for effective utilization by the organization.  Leveraging the concepts of agile innovation (Wilson & Doz, 2011), these processes include attracting, foraying, and experiencing.


Attracting is a process designed to bring ideas into the organization from outside.  What may once have been the sole domain of customer experience surveys and suggestion boxes has evolved rapidly over the last few years.  Today, attracting is commonly achieved via communities of excellence, online community forums, and crowdsourcing platforms.

Leveraging the concepts of agile innovation, these processes include attracting, foraying, and experiencing.


While attracting, once initiated, is mostly a passive activity managing the influx of ideas and opportunities, foraying is a much more active activity.  Foraying is a process where individuals within the organization seek out and discover information and ideas.  This can be formalized as business development activities, or accidental as part of the normal business interactions with customers, vendors, or other employees.  Foraying requires more direct involvement in uncovering potential ides.


On the opposite end of the spectrum from attracting is experiencing.  Some knowledge and ideas cannot be fully understood or mastered without experiencing them directly.  This is particularly true with tacit knowledge, or ideas rooted intricately in the culture or context in which they originated.

Is the Juice Worth the Squeeze?

The process to capture, promote, and execute on the different types of knowledge requires varying degrees of effort.  For instance, Wilson and Doz suggested explicit knowledge was easiest to capture through an attracting process similar to virtual communities (VC’s) or crowdsourcing approaches (Hammon & Hippner, 2012; Schröder & Hölzle, 2010). VC’s provide an organization a simple, cost-effective method for capturing the innovative ideas of the masses.  At the same time, this easily codified and transmitted knowledge can also be easily stolen or replicated by competitors, diminishing its competitive value.

On the other end of the spectrum, Wilson and Doz argued tacit knowledge can only be acquired through an experiencing process involving greater time and investment targeted at specific markets, challenges, and geographies. This tacit knowledge is more difficult and expensive to obtain. Yet, because of the time and investment, it is also much more difficult for competitors to replicate, which means it also holds much greater strategic value.

Organizations need to develop integrated methods of accessing and converting these resources into viable products and service opportunities, suggesting processes cannot only be for ideation, but also for the selection and continued development of innovative solutions.  At the same time, they must fully understand the risks and rewards associated with the means of accumulating and developing that knowledge.  This is a critical element of innovation success.

Fitting Process into the Innovation Strategy Framework

Innovation processes represent the specific tools through which the organization engages knowledge resources in ideation and execution (black boxes in Figure 1). Per Wilson and Doz, these are not mutually exclusive, but represent potential means of engaging knowledge resources as required in the development of innovative solutions.  For instance, ideation might be achieved using VC’s (attracting), but further development might require rapid prototyping (Sandmeier, Morrison, & Gassmann, 2010; Tuulenmäki & Välikangas, 2011) using direct, on-site customer engagement (foraying), or long-term development within market (experiencing).  Likewise, innovative ideas developed through experiencing might be tested in different markets (foraying) or through crowdsourced selection processes (attracting).  The processes are the formalized ways in which the people across the model interact; they are the tools of innovation selection and development.


The Innovation Strategy Framework

Furthermore, successful innovation practices require the continued application of knowledge resources from ideation through execution.  The prospect of failure is not only inherent to innovation, failure is a likely part of innovation execution (McGrath, 2011).  McGrath encouraged organizations to embrace the learning opportunities inherent in innovation execution, proposing the use of small-scale experimentation to minimize large-scale failure.  The application of rapid-prototyping and extreme programming processes to new product and service development promote similar practices (Abele, 2011; Sandmeier et al., 2010; Tuulenmäki & Välikangas, 2011).  Sandmeier et al. investigated the use of extreme programming practices in the development of innovative products, and found the early and continued involvement of diverse external knowledge resources was positively related to successful innovation.  Tuulenmäki and Välikangas extolled the similar value of rapid prototyping and experimentation in the successful execution of innovative solutions.  Each of these perspectives suggest the continued inclusion of an organization’s knowledge resources throughout innovation execution to refine and develop optimal solutions.   As a result, the use of attracting, foraying, and experiencing processes to leverage an organization’s knowledge network does not end once ideation is complete, but must be integrated across the entire innovation process.

Opening the entire innovation process to actors beyond the direct control of the organization requires significant dedication from an organization’s leadership.  Developing a culture of innovation is the final major element of innovation strategy.




Abele, J. (2011). Bringing minds together. Harvard Business Review, 89(7–8). Retrieved from https://hbr.org/

Hammon, L., & Hippner, H. (2012). Crowdsourcing. Business & Information Systems Engineering, 4(3), 1–166. http://doi.org/10.1007/s12599-012-0215-7

Mahroeian, H., & Forozia, A. (2012). Challenges in managing tacit knowledge: A study on difficulties in diffusion of tacit knowledge in organizations. International Journal of Business and Social Science, 3(19), 303–308. Retrieved from http://ijbssnet.com/

McGrath, R. G. (2011). Failing by design. Harvard Business Review, 89(4), 76–83. Retrieved from http://hbr.org/

Sandmeier, P., Morrison, P. D., & Gassmann, O. (2010). Integrating customers in product innovation: Lessons from industrial development contractors and in-house contractors in rapidly changing customer markets. Creativity and Innovation Management, 19(2), 89–106. http://doi.org/10.1111/j.1467-8691.2010.00555.x

Schröder, A., & Hölzle, K. (2010). Virtual communities for innovation: Influence factors and impact on company innovation. Creativity and Innovation Management, 19(3), 257–268. http://doi.org/10.1111/j.1467-8691.2010.00567.x

Tuulenmäki, A., & Välikangas, L. (2011). The art of rapid, hands-on execution innovation. Strategy & Leadership, 39(2), 28–35. http://doi.org/10.1108/10878571111114446

Wilson, K., & Doz, Y. L. (2011). Agile innovation: A footprint balancing distance and immersion. California Management Review, 53(2), 6–26. http://doi.org/10.1525/cmr.2011.53.2.6

Fear Not the AI Overlords! Humans Have Intrinsic Value.

There is significant hype about Artificial Intelligence (AI) and its potential to take over many jobs thought safe from Automation.  It has been suggested AI could replace accountants, lawyers, doctors, and even general management activities.  While it is true that advances in AI will certainly change many jobs, as so often happens, the fear is exaggerated.  First, there is no evidence to support the notion automation has ever eliminated more jobs than it has created.  Second, and more importantly, humans have intrinsic value that is unlikely to ever be replicated or replaced.

The Fear of Losing Jobs

Before anyone gets too excited, a recent Wall Street Journal article highlights the facts of mass automation in the past.  Technology from the cotton gin through AI has always eliminated some jobs, but historically it has also created far more and better paying jobs as a result.  Sure surrey drivers were put out of work with the advent of the automobile, but the auto industry created millions of jobs supporting the US GDP for decades.  AI is simply the latest in a long-line of technological advances feared to lead to the end of our society.  It has never happened before and is unlikely to happen anytime soon.  It is true that some jobs may cease to exist, but this will be accompanied by a growth of new jobs supporting the AI industry.  Even more remarkable will be the new jobs that don’t even exist today.

A recent report from the Institute for the Future estimates 85% of the jobs today’s students will perform by the year 2030 haven’t yet been invented.  This is a difficult prospect for today’s workers to imagine, but it is not without precedent.  Student’s graduating high school in the 1990’s could not have imagined careers working in web design, social media, or – for that matter — artificial intelligence, machine learning, and big data.  Another recent article from MIT Sloan Management Review hints at some of the new jobs AI technology may create.

On top of all of that, it is unlikely many of the jobs being predicted to succumb to AI will actually go away.  It is much more likely they will be augmented and changed than disappear entirely.  And the reason is simple: humans have innate value in performing jobs in a human society.

Humans Have Intrinsic Value

Although AI is redefining what is considered automata by allowing more variation in performance, it is still not human.  Human beings are defined by the irrational and emotional more than they are by cold, calculated precision.  While this may seem to be a negative aspect of humans, it is also the source of the innovation, creativity, and passion that simply cannot be replicated.  Just for sake of argument, let’s examine just one of the jobs proposed to be replaced in the future by AI: management.

Business management is an oft misunderstood discipline, which does not benefit from the HR moniker “people manager”.  You manage objects, but you lead people.  Objects are managed to gain efficiency, but they have finite limitations. You cannot encourage a robot to be more productive.  You cannot ignite passion in your inventory tracking software to go above and beyond.  Yet human beings have nearly limitless capability to “reach for a goal”, “put in extra effort”, or “embrace shared visions”.  While this can also work to reduce human performance (as discussed in this article from MIT Sloan Management Review), this is critical distinction when looking at the effects of AI in particular.

Management, in its truest sense, is absolutely ripe for AI replacement.  Eliminating the idiosyncracies of human performance can have significant value to organizations.  AI is simply better able to gather, process, and act on vast amounts of data where human input is less vital (although not necessarily irrelevant).  By offloading these tedious and taxing responsibilities, while also improving their performance, humans can spend more time doing the things where they have intrinsic, and irreplaceable value (See article from Swiss Cognitive).

Leadership, on the other hand, will no longer need to take backseat to management.  By focusing on leadership, organizations will not only gain the advantages of AI-based management efficiency, but also from the benefits of stronger human performance.  In essence, organizational leaders will be able to offload the tasks they don’t do very well anyway, and focus on the actions that lead to truly superior performance.

Fear Not!!

While the example above focuses on my area of expertise, the same can be said for many other jobs ripe for AI augmentation.  AI, like the cotton gin and automobile before it, are tools that will augment and improve the way we work.  Yes, some jobs may be significantly reduced or eliminated; however, they will be replaced by newer and better jobs.  The jobs getting augmented by AI will simply change, putting more focus on the human aspect.  It is not the end of the world.