Author: KJ (Ken) Salchow, Jr.

Dr. Salchow is the senior program manager for professional certification at F5 Networks. He received his Bachelor’s from MN School of Business and his MBA and DBA from Capella University. Personally, his focus is on the evaluation and development of expert performance among knowledge workers as the source of strategic innovation in globally competitive markets. Professionally, his current focus is on elevating the value and credibility of technology certifications and developing reliable measures of said value for participants, sponsors, and employers. 

Innovation, Meet Strategy

Just like strategy, being innovative or creative is useless unless ideas can be executed on.

While academics and practitioners alike still argue over whether innovation can be dictated and prescribed, these arguments tend to revolve around the act of creativity or inspiration, rather than the process of innovation.  Just like strategy, being innovative or creative is useless unless ideas can be executed on.  The innovation strategy framework does not attempt to regulate the act of inspiration, only to provide an environment fostering both the identification of ideas as well as the development of these ideas into successful projects.

InnovationModel

Figure 1. The Innovation Strategy Framework

One of the single greatest challenges in developing sustainable innovation practices is not the lack of ideas, but the inability to focus on those ideas most likely to succeed, both in the market as well as within the organization.  Organizations are awash in good ideas, but lack the resources and capabilities to develop all of them into viable products, services, and processes.  Rather than selecting and curating the best ideas, the ones most aligned with the organization’s capabilities and direction, ideas are often selected solely at the whims of organizational influencers using idiosyncratic criteria.  Innovation tends to be born out of circumstance rather than cognizance.

This is where strategic domains fit into the innovation strategy framework (figure 1).

Strategic Innovation Domains

Every company has a set of strategic goals, either explicit or implicit.  These strategic goals help orient the organization, prioritize spending, and become the yardstick by which success is measured.  It only makes sense that these same goals should help an organization identify, develop, and manage its innovation efforts.  Strategic innovation domains align the innovation process with the overall organization acting as agents in the innovation process to seek out innovation opportunities contributing to organizational success.

The inclusion of strategic innovation domains is the key difference between the innovation strategy framework and many other approaches to sustainable innovation.  Innovation is frequently addressed as something that everyone in the organization should do, or be a part of.  Again, this is likely a result of confounding ideation from execution; however, even regarding ideation, expecting organizational participants to be more innovative or creative simply because they are told to do misses the mark.  Employees see this as another task they are expected to perform in addition to their existing duties, without additional compensation.  In addition, this approach is based on the notion that organizations lack ideas, rather than lacking a focused approach to seeking out and executing on the right ideas.

Applying Focus to Innovation Efforts

Strategic domains focus on innovation execution.  Each strategic domain is responsible for identifying, developing, and managing innovation aligned with its strategic focus.  Rather than relying on happenstance to surface the right innovation ideas, strategic domains leverage the innovation processes (attracting, foraying, and experiencing) to seek out appropriate ideas as well as develop the most promising ones.  There are several benefits to this approach.

Imagine if an organization refused to invest in accounting and just told employees to be more accountable.

The most obvious benefit is the alignment of innovation and the strategic trajectory of the organization.  There should be as many strategic innovation domains as there are strategic goals within the organization.  If the organizational strategy changes, then the strategic innovation domains should change to reflect this.  In this manner, the organization’s innovation efforts are focused on those most likely to fit within the organization’s capabilities and long-term focus.  This significantly reduces the signal to noise ratio by eliminating those ideas unlikely to be successfully executed by the organization.

By making an individual (or group) specifically responsible for innovation efforts, you also gain ownership.  If everyone is responsible for innovation, then no one can be held responsible for the success or failure.  Using the innovation pipeline approach of the strategic innovation framework, the innovation efforts of each strategic domain can be measured; and, that which can be measured, can be managed.  Strategic domains make innovation just as much a part of business operations as HR, accounting, sales, or product management.

Finally, making innovation the primary function of an individual (or individuals), leads to more consistent performance.  Making innovation “everyone’s job” in addition to their existing roles means either innovation or operations will suffer when the two objectives compete.  Employing people with dedicated innovation functions ensures continued focus on developing innovation and raises the chances of success significantly.

Importance of Strategic Domains

The number of organizations believing they will be successful with innovation without investing is staggering.  This would be a ridiculous approach to any other business activity.  Imagine if an organization refused to invest in accounting and just told employees to be more accountable.  Yet, when it comes to innovation, leaders somehow assume that telling employees to be more innovative will somehow launch the next market-leading product or service.   Investing in strategic innovation domains not only creates an environment where innovation can succeed, it also telegraphs the organization’s commitment to being successful.  It’s easy to say you believe in the importance to innovative, but until you are willing to invest, it is hard to believe it.

 

 

Google Does Not Obviate “Knowing”

There is a strange notion making the rounds of social media in various forms, used to argue against traditional learning and assessment standards.  This reoccurring theme suggests the ubiquitous ability to leverage Google search, Wikipedia, or other online resources to find answers obviates the need to learn anything for yourself.  I.e., if we need to know something, we can just look it up in real-time and don’t need to waste time learning this information before we need it.  This theme has come up in discussions of our educational system curriculum, the supposed uselessness of standardized testing, and even in employee assessment criteria.

The Internet was never intended to be a replacement for independent knowledge.

Perhaps this is a special case of the Dunning-Kruger effect (Dunning, Johnson, Ehrlinger, & Kruger, 2003; Kruger & Dunning, 1999), but there are at least two clear reasons why access to knowledge is not equivalent to actually knowing it.  The first is a complete disconnect from the way human beings develop skill and competency.  The second is the assumption real-time knowledge, although ubiquitous, is accurate and will always be available.

Having Facts is Not “Knowing”

The most incongruous part of this idea is the assumption that knowledge is the result of just having a bunch of facts.  Thus, if you can just look up the facts, you have knowledge.  Unfortunately, unlike in the Matrix, human beings cannot simply download competence and expertise.

Learning something, and becoming good at it, is a process of building mental models on top of the foundation of rote facts

The study of experts and expert knowledge has well established the difference between experts and novices is not in what they know (the facts), but in how they apply those facts. It is based on how each fact fits with other facts or other pieces of knowledge. Expertise is the result of a process of integrating facts, context, and experience together and defining more refined and efficient mental models (Ericsson, 2006).  Learning something, and becoming good at it, is a process of building mental models on top of the foundation of rote facts.  This cannot be done without internalizing those facts.

In addition, returning to Dunning-Kruger, without building competence, individuals are incapable of discerning the veracity of individual facts.  Our ability to understand whether information is accurate, or of any substance, results from being able to rectify new information with our existing mental models and knowledge.  Those with less competence are the most unable to evaluate this information making them the most susceptible to not only accepting incorrect information as fact, but also of developing mental models incorrectly reflecting reality.

Limits of Ubiquitous Knowledge Access

Although those of use living in developed economies take ubiquitous access to knowledge for granted, this is not the case for all human beings, nor is it guaranteed to always exist.  It is estimated only about 50% of the world’s population is connected to the Internet, over two-thirds of which are in developed economies.  Even these figures bear further investigation, as those in developing countries with Internet access are far more likely to be connected by slower, less reliable means keeping their access from being truly ubiquitous.  Furthermore, while China contributes significantly to the world’s total Internet users, the Chinese government does not allow full, unrestricted access to the knowledge available via the Internet.  This leaves the number of people with true, ubiquitous access well below 50% of the population.

Even for those of us fortunate enough to have nearly ubiquitous access to an unrestricted Internet of knowledge, access is fragile.  Power outages as a result of simple failure, natural events, or even direct malice, can immediately render information inaccessible.  Emergency situations where survival might rely on knowledge also often exist outside the bounds of this seemingly ubiquitous access. Without a charge, or cellular connection, many find themselves ill-equipped to manage.

Dumbing Down our Society

The idea that access to knowledge is the same as having knowledge portends a loss of intellectual capital.  Whereas societies in the past have maintained control by limiting access to information, we are creating a future where control is maintained by delegitimizing and devaluing the accumulation of knowledge through full access to information.  We are positioning society to fail in the future because they will have not only become dependent on being spoon-fed information instead of actual learning, but will have also lost the ability to differentiate fact from fiction.

Not only is the idea that access to knowledge equates to having knowledge founded on shaky foundations lacking any kind of empirical basis, it undermines the actual development of knowledge

Although it would be nice to assume this is a dystopian view of the future, we are already seeing the effects of this process.  As social media becomes increasingly the way our society views the world around us, we can already see how ubiquitous access to information is affecting our perceptions of the world around us.  Without the ability to think critically, something only developed through the accumulation of knowledge and experience, in evaluating the real-time information we receive, our society is being manipulated into perspectives not of our own choosing, but the choosing of others.  We are losing the ability to process the information we receive and find ourselves increasingly caught in echo-chambers only presenting information supporting potentially incorrect world-views.

The Internet was never intended to be a replacement for independent knowledge.  It was developed to expand our ability to access information in the pursuit of developing knowledge and capability.  Not only is the idea that access to knowledge equates to having knowledge founded on shaky foundations lacking any kind of empirical basis, it undermines the actual development of knowledge.

 

 

Resources

Dunning, D., Johnson, K., Ehrlinger, J., & Kruger, J. (2003). Why people fail to recognize their own incompetence. Current Directions in Psychological Science, 12(3), 83–87. http://doi.org/10.1111/1467-8721.01235

Ericsson, K. A. (2006). An introduction to Cambridge handbook of expertise and expert performance: Its development, organization, and content. In The Cambridge handbook of expertise and expert …. New York, NY: Cambridge University Press.

Kruger, J., & Dunning, D. (1999). Unskilled and Unaware of It : How Difficulties in Recognizing One’s Own Incompetence Lead to Inflated. Journal of Personnality and Social Psychology, 77(6), 1121–1134. http://doi.org/10.1037/0022-3514.77.6.1121

 

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.

 

References

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.

References

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

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.

Foraying

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.

Experiencing

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.

InnovationModel

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.

 

 

References

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

Improving Multiple-Choice Assessments by Limiting Time

Standardized, multiple-choice assessments frequently come under fire because they test rote skills, rather than practical, real-world application.  Although this is a gross over-generalization failing to account for the cognitive-complexity the items (questions) are written to, standardized assessments are designed to evaluate what a person knows, not how well they can apply it.  If that were the end of the discussion, you could be forgiven in assuming standardized testing is poor at predicting real-world performance or differentiating between novices and more seasoned, experienced practitioners.  However, there is another component that, when added to standardized testing, can raise assessments to a higher level: time.  Time, or more precisely, control over the amount of time allowed to perform the exam, can be highly effective in differentiating between competence and non-competence.

The Science Bit

Research in the field of expertise and expert performance suggests experts not only have the capacity to know more, they also know in a way differently than non-experts; experts exhibit different mental models than novices (Feltovich, Prietula, & Ericsson, 2006).  Mental models represent how individuals organize and implement knowledge, instead of explicitly determining what that knowledge encompasses.  Novice practitioners start with mental models representing the most basic elements of the knowledge required within a domain, and their mental models gradually gain complexity and refinement as the novice gains practical experience applying those models in real world performance (Chase & Simon, 1973; Chi, Glaser, & Rees, 1982; Gogus, 2013; Insch, McIntyre, & Dawley, 2008; Schack, 2004).

While Chase and Simon (1973) first theorized that the way experts chunk and sequence information mediated their superior performance, Feltovich et al. (2006) suggested these changes facilitated experts processing more information faster and with less cognitive effort contributing to greater performance. Feltovich et al. (2006) noted this effect as one of the best-established characteristics of expertise and demonstrated in numerous knowledge domains including chess, bridge, electronics, physics problem solving, and medical applications.

For example, Chi et al. (1982) determined that the way novices and experts approach problem-solving in advanced physics was significantly different despite all subjects having the same actual knowledge necessary for the problem solution; novices focused on surface details while experts approached problems from a deeper, theoretical perspective.  Chi et al. also demonstrated the novice’s lack of experience and practical application contributed to errors in problem analysis requiring more time and effort to overcome. While the base knowledge of experts and novices may not differ significantly, experts appear to approach problem solving from a differentiated perspective allowing them more success in applying correct solutions the first time and recovering faster when initial solutions fail.

In that vein of thought, Gogus (2013) demonstrated that expert models were highly interconnected and complex in nature, representing how experience allowed experts the application of greater amounts of knowledge in problem solving.  The ability for applying existing knowledge with greater efficiency augments the difference in problem-solving strategy demonstrated by Chi et al. (1982).  Whereas novices apply problem-solving approaches linearly one at a time, experts evaluate multiple approaches simultaneously in determining the most appropriate course of action.

Achieving expertise is, therefore, not simply a matter of accumulating knowledge and skills, but a complex transformation of the way experts implement that knowledge and skill (Feltovich et al., 2006). This distinction provides clues into better implementing assessments to differentiate between expert and novice: the time it takes to complete an assessment.

Cool Real-World Example Using Football (Sorry. Soccer)

In an interesting twist on typical mental model assessment studies, Lex, Essig, Knoblauch, and Schack (2015) asked novice and experienced soccer players to quickly and accurately decide the best choice of tactics (either “a” or “b”) given a video image of a simulated game situation.  Lex et al. used eye-tracking systems to measure how the participants reviewed the image, as well as measuring their accuracy and response time.  As one would expect, the more experienced players were both more accurate in their responses, as well as quicker. Somewhat surprising was the reason experienced players performed faster.

While Lex et al. (2015) determined both sets of players fixated on individual pixels in the image for nearly the same amount of time, experienced players had less fixations and observed less pixels overall.   Less experienced players needed to review more of the image before deciding, and were still more likely to make incorrect decisions.  On the other hand, more experienced players, although not perfect, made more accurate decisions based on less information.  The difference in performance was not attributable to differences in basic understanding of tactics or playing soccer, but the ability of experienced players to make better decisions with less information and taking less time.

The Takeaway

Multiple-choice, standardized assessments are principally designed to differentiate what people know, with limited ability to differentiate how well they can apply that knowledge in the real world.  Yet, it is also well-established that competent performers have numerous advantages leading to better performance in less time.    If time constraints are actively and responsibly constructed as an integral component of these assessments, they may well achieve better predictive performance; they could do a much better job of evaluating not just what someone knows, but how well they can apply it.

 

References

Chase, W. G., & Simon, H. A. (1973). The mind’s eye in chess. In Visual Information Processing (pp. 215–281). New York, NY: Academic Press, Inc. http://doi.org/10.1016/B978-0-12-170150-5.50011-1

Chi, M. T. H., Glaser, R., & Rees, E. (1982). Expertise in problem solving. In R. J. Sternberg (Ed.), Advances in the psychology of human intelligence (Vol. 1, pp. 7–75). Hillsdale: Lawrence Erlbaum Associates.

Feltovich, P. J., Prietula, M. J., & Ericsson, K. A. (2006). Studies of expertise from psychological perspectives. In The Cambridge handbook of expertise and expert …. New York, NY: Cambridge University Press.

Gogus, A. (2013). Evaluating mental models in mathematics: A comparison of methods. Educational Technology Research and Development, 61(2), 171–195. http://doi.org/10.1007/s11423-012-9281-2

Insch, G. S., McIntyre, N., & Dawley, D. (2008). Tacit Knowledge: A Refinement and Empirical Test of the Academic Tacit Knowledge Scale. The Journal of Psychology, 142(6), 561–579. http://doi.org/10.3200/jrlp.142.6.561-580

Lex, H., Essig, K., Knoblauch, A., & Schack, T. (2015). Cognitive Representations and Cognitive Processing of Team-Specific Tactics in Soccer. PLoS ONE, 10(2), 1–19. http://doi.org/10.1371/journal.pone.0118219

Schack, T. (2004). Knowledge and performance in action. Journal of Knowledge Management, 8(4), 38–53. http://doi.org/10.1108/13673270410548478

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.

 

 

“Zone to Win” Missing Critical Elements of Innovation Strategy

Although Zone to Win: Organizing to Compete In an Age of Disruption by Geoffrey Moore is a wonderful framework for the management of organizational resources to both lead, as well as survive, disruptive innovation, there are some missing elements that might prove useful to readers.  Specifically, Moore focuses on management, and although absolutely necessary, management does not supersede strategy or innovation in long-term success.  Zone to Win fails to encompass appropriate innovation strategy in two ways: the idea of disrupting your own organization, and gap between innovation ideation and implementation.

The Strategy of Disrupting Your Own Organization

Moore seems uncharacteristically blunt on the idea of organizations needing to disrupt their own business.  Moore suggests “all that stuff about how you have to learn to disrupt ourself–it’s baloney. It can’t be done.”  Moore’s contention is that it is impossible for an established business to replace one business model with another; yet, later in the book documents the success of organizations like Adobe and Microsoft that have radically changed their business models to deliver software via subscription instead of perpetual licensing.  It turns out Adobe and Microsoft are two excellent examples for exploring why it is absolutely important for organizations to engage in deliberate acts of self-disruption.  Adobe intentionally disrupted their existing business model (a highly profitable one) before anyone else could eat their lunch; Microsoft changed theirs only after Google started taking away their business.  Adobe was seen as a leader; Microsoft as a laggard.

From an organizational strategy standpoint, it is absolutely critical to develop strategic scenarios of how the future world may look, and how those changes may affect your existing business model.  Scenario planning provides context that is essential when disruption happens, but more importantly, can presage potential disruptions before they happen.  If scenario planning illuminates a significant threat, the organization has only two options: disrupt their own business in order to transfer old business to new business; or, let someone else start eating away your customer base.  It can be argued that cannibalizing your own business, if only to prevent someone else from doing so, makes sense in its own right.  If that new business also portends an era of growth, more the better.

Working to disrupt your own business should not be an organization’s only focus, but failure to even contemplate it, or act upon it, is a strategic failure.

Moore does cover how an organization can respond to disruptive innovations once the initial attacks begin and makes an excellent case for how to manage those attacks.  However, by dismissing, out of hand, the idea that an organization can lead those disruptions, and thus avoid the attack in the first place, Moore seems to completely discount the value of doing so.  While a brilliant theorist like Moore likely meant to dissuade companies from trying a wholesale rip-and-replace of their business model (not something any serious innovation strategist would propose), simply dismissing the strategic value of self-disruption seems cursory.  Moore’s stance is even more glaring considering the zone management framework he proposes actually makes it possible to disrupt your own business in a structured, well-managed way.

Working to disrupt your own business should not be an organization’s only focus, but failure to even contemplate it, or act upon it, is a strategic failure.

Mind the Gap

Another topic which gets too little attention in Zone to Win, is the gap between innovation ideation and the development of the Incubation Organizational Units (IOUs) suggested to incubate and develop promising innovation efforts.  Here again, Moore proposes management organization and governance brilliantly, but only once the innovation ideas get to the point of being a well-formed business proposals.  What is missing is the innovation strategy to get from ideation to the point of proposal.  Aside from a brief mention of internal R&D or other means, Moore fails to specify where this fits in the zone management framework, or how to appropriately fund it.

There is substantial research supporting the notion that “having ideas” is not the challenge for most established organizations.  On the contrary, the most commonly cited challenge is in identifying ideas with potential, and exploring them to the point where they can be taken as serious projects for the organization.   This process needs to be budgeted for, managed, and held accountable in order to be successful.  Without it, the only projects that move forward will be those lucky enough to have a champion with the authority, clout, and budget to develop them.  This creates a choke point to innovation.

Yet, it misses the fact most start-ups don’t start life with venture capital; they start with funding from the founders and personal investors until they have something they can pitch to those VCs.

Moore’s framework suggests treating IOUs as “start-up” companies and funding them just like venture capitalists (VCs) would, which is (again) a brilliant way to manage well-formed ideas.  Yet, it misses the fact most start-ups don’t start life with venture capital; they start with funding from the founders and personal investors until they have something they can pitch to those VCs.  This stage in the innovation cycle is critical and where most start-ups fail.  It is the large end of the funnel.  In such a well thought-out framework as Zone to Win presents, missing this critical element is disappointing.

Appropriately funding and managing the initial R&D neccessary to initiate innovation is just as critical to success as any other component.  However, the idea of funding pure R&D is not common among many of the organizations that would most benefit from taking Moore’s framework to heart.  Even technology organizations often do this as skunkworks or “off-the-books” projects with little organization, governance, or metrics.  Moore’s failure to address this misses a critical element in successfully leading disruptive innovation.

A Step in the Right Direction

Barring these two criticisms, Moore’s work has certainly cemented his place in the annals of business gurus and shows a continuing dedication to helping organizations overcome their own success.  The zone management framework provides a blueprint for overcoming the obstacles Clayton Christensen (and colleagues) frequently cites as the downfall of established organziations in light of disruptive innovation.  Most importantly, Moore adds significant credibility to the idea that innovation can only be truly successful if: a) it is done outside of the main business (incubation versus performance zones); and, b) it is treated with the same care and dedication used to manage any business process.   By addressing the importance of strategic planning, and formally defining the R&D component, zone management would be even better.

Lastly, one word of warning to devotees seeking to implement zone management. Remember the words of Michael Porter: “strategic positioning, means performing different activities from rivals’ or performing similar activities in different ways (1996, p. 62).  Frameworks like Moore’s are effective tools, but without making them your own, they lose their strategic value.  This is where leadership takes over from management.

References

Porter, M. E. (1996). What is strategy? Harvard Business Review, 74(6), 61–78. Retrieved from http://hbr.org/

 

The Effects of Positive Psychology on Organizational Success

Intent is a powerful mediator of outcomes.  An organization’s mission, vision, and values set the direction of future success simply by codifying the organization’s intent.  This intent flows through every aspect of the organization, affecting the choices people make, and the outcomes of those actions.  (Read Simon Sinek’s “Start with Why“).

For instance, suppose you start a company to develop a better mousetrap.  You might start this company simply because we all know that if you build a better mousetrap, the world will beat a path to your door and generate huge profits.  On the other hand, you might start your company because you have a passion for eradicating the scourge of mice and their negative effects on human society.   These are both valid rationales for starting a new company and developing a better mousetrap.  If you succeed in building a better mousetrap, both intents are likely to be successful, at least initially.  Intent, however, will begin to show over time.  The company whose intent is purely profit driven will make choices supporting that mission, while the company whose intent is based on a passion for improving the world through mouse eradication will make different choices supporting that mission.  These choices affect the long-term viability of the business.

Not only will these differences in intent affect things like pricing, marketing, customer experience, and other traditional business decisions, they will also affect the people in the organization.  A positive intent keeps employees engaged and helps them grow as people and employees.  These are the effects of positive psychology and should not be discounted when an organiation considers why they do what they do.

Understanding Positive Psychology

Positive psychology, in short, is simply a focus to understand and investigate the positive capabilities and achievements of the individual, community/organization and society as a whole (Fredrickson, 2001; Quinn, Dutton, & Cameron, 2003; Seligman & Csikszentmihalyi, 2000; Sheldon & King, 2001).  Seligman and Csikszentmihalyi (2000), suggest that, prior to WW II, psychology was normally associated with three purposes: treating mental illness, encouraging the development and growth of all people, and the identification and development of exceptional capabilities. Since WW II, however, the focus of psychology has been solely on mental illness, deficit and pathology.  The other, more positive implications of psychology withered. While positive psychology is not intended to supplant existing research and theories on mental illness (Sheldon & King, 2001), it suggests that the typical deficit models practiced for over half a decade fail to adequately describe the realities of the population as a whole, as well as be a reminder of the original tenets of psychology foundation to include the good, the exceptional and the positive.

This positive approach has spawned any number of new approaches to the understanding of human development and organizational development focused on examining the positive instead of the negative.  For instance, towards the understanding of human development, theories have evolved to demonstrate that positive emotions or moments not only broaden an individuals perceptions of the world, but also build capabilities and personal resources which help them mitigate the effects of negative experiences (Fredrickson & Losada, 2005).  Additional research suggests this broadening capability is not solely constrained to mental perceptions, but to physiological perceptions as well like visual attention and field of view (Fredrickson, 2013). The resources accumulated from positive moments may not be abstract resources related to resilience and adaptability, but more discrete resources like attention to detail and capacity to learn.

Towards understanding organizational development, positive psychological approaches have generated new ways at looking at the process of creating exceptional organizations, not by fixing what is wrong, but by amplifying what is right.  Appreciative Inquiry is a model for change practices making the assumption that all organizations have an essentially positive capability to succeed.  By examining the past moments of peak performance, achievement and success, the organization can create a vision of the future based on those positive aspects (Quinn et al., 2003).

The common element in all of these ideas is that there is benefit in focusing on what is good instead of what is bad.  Focusing on the positive aspects creates a upward spiral of reinforcement (Quinn et al., 2003) and is a self-perpetuating process under normal circumstances (Fredrickson, 2013).  The corollary is that a focus on the bad would promote a downward, self-perpetuating cycle.  Thus, attempting to make change by focusing on the negative aspects of  what not to do, while one appropriate response to challenges, creates a self-defeating process promoting blame, low self-worth and incompetence.  Positive psychology suggests that focusing on what to do, on what was successful, is a better alternative as it creates a self-fulfilling cycle promoting excellence, success and achievement.

Brining it back to Intent

One could argue that a profit intent is not inherently bad; without making money, companies cannot sustain themselves.  Yet, without a positive focus beyond profit, without an intent that can inspire and create positive feelings, the organization is likely to diminish in productivity, innovation, and, ultimately, profit.  The concepts of positive psychology push us to appreciate a focus on the existence of the extraordinary and exceptional instead of simply on what is broken and dysfunctional (Seligman & Csikszentmihalyi, 2000).  It is an attempt to look at the positive potential of the future instead of simply examining the future as an attempt to overcome deficit.  It tells us that our intent has significant influence on our long-term outcomes.

References

Fredrickson, B. L. (2001). The role of positive emotions in positive psychology. The broaden-and-build theory of positive emotions. The American psychologist, 56(3), 218–226.

Fredrickson, B. L. (2013). Updated thinking on positivity ratios. The American psychologist, 68(9), 814–22. doi:10.1037/a0033584

Fredrickson, B. L., & Losada, M. F. (2005). Positive affect and the complex dynamics of human flourishing. The American psychologist, 60(7), 678–86. doi:10.1037/0003-066X.60.7.678

Quinn, R. E., Dutton, J. E., & Cameron, K. S. (2003). Positive Organizational Scholarship : Foundations of a New Discipline. San Francisco, CA: Berrett-Koehler. Retrieved from http://ezproxy.library.capella.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=nlebk&AN=260674&site=ehost-live&scope=site

Seligman, M. E. P., & Csikszentmihalyi, M. (2000). Positive psychology: An introduction. American Psychologist, 55(1), 5–14. doi:10.1037//0003-066X.55.1.5

Sheldon, K. M., & King, L. (2001). Why positive psychology is necessary. The American psychologist, 56(3), 216–7. doi:10.1037/0003-066X.56.3.216

The Three “People” Needed for Successful Innovation

Despite decades of research into the constructs of innovation, few practical sources of sustained innovation have proven causal to organizational success.  A cursory examination of innovation theory fails to provide concrete evidence that innovation, in itself, is key to long-term success; most innovation theories rely on post-hoc analysis of firm performance focused on successes, rather than failures (Buisson & Silberzahn, 2010; Burke, van Stel, & Thurik, 2010).  The term innovation is just as difficult to articulate being equally evaluated through ex post selection of successful innovation rather than innovative efforts in general.  While innovation may not guarantee firm success, it is clear that organizations failing to adopt to the pace of the modern, global marketplace will flounder (Reeves & Deimler, 2011); the chances of success increase dramatically if organizations are positioned to innovate and change in response.  As such, understanding the models, systems, and approaches improving an organization’s ability to innovate are increasingly important even if they are not proven to promote long-term success, or even successful innovation. This is the basis of the Innovation Strategy Framework, which attempts to combine multiple theories of innovation into a single construct.

InnovationModel

Figure 1. The Innovation Strategy Framework

Today, we are going to look at the importance of people to innovation strategy, what those people do, where they come from, and why they are important to innovation.

Human capital, or the knowledge, skills, abilities and other characteristics (KSAO’s) of the people associated with the organization are the source of innovation.  Human capital is a critical starting point and requirement for the development of organizational knowledge and innovation capabilities (Choong, 2008; Ployhart, Nyberg, Reilly, & Maltarich, 2014).  This is not just the employees of the organization, but also the knowledge resources of partners and other collaborators.  The greater the diversity and density of these knowledge resources, the greater the potential for organizations to achieve innovative outcomes (Dell’Era & Verganti, 2010; Phelps, 2010).   Clearly, the depth, breadth, and quality of the people in the organization are critical dimensions of innovation capability.

The three main groups of people necessary for successful, serial innovation capability are: the people with innovation ideas, the people nurturing and developing ideas into successful innovation, and the organizational leadership fostering innovation.

People as the Source of Ideation

The largest group of people involved with innovation is the infinite sources of innovation ideas (left side of Figure 1).  This group of people, including customers, partners, employees, and others, are the heterogeneous sources of knowledge providing innovative ideas and solutions.  These resources are both internal and external, creating the depth, breadth, and diversity of knowledge to supply the organization with innovative fuel (Dell’Era & Verganti, 2010; Phelps, 2010; Rothaermel & Hess, 2010).  The composition of an organization’s knowledge ecosystem (employees, partners, customers, and others) significantly contributes to the ability of an organization to successfully innovate (Dell’Era & Verganti, 2010; Engel & Del-Palacio, 2011; Kim & Ployhart, 2014; Phelps, 2010; Rothaermel & Hess, 2010; Sandmeier, Morrison, & Gassmann, 2010; Wilson & Doz, 2011).

Interestingly, while this is the largest group of people directly involved with innovation efforts, they are not necessarily the most important.  Yet, many organizations embark on innovation efforts by encouraging their employees to “be more creative”, or “be innovative”.   To the contrary, research suggests most organizations have far more innovation ideas than they can possible deal with; the problem is selecting and developing ideas into real-world solutions.  While organizations need to encourage innovation and creativity, making it the primary focus of innovation efforts will fail more often than succeed. The belief that innovation stems from the rare, perfect idea is a pervasive myth.

Ideation is essential, but completely useless without the other people necessary for innovation success.

People Strategically Selecting and Developing Innovation

On the right side of Figure 1, people in the strategic domains represent the knowledge resources responsible for taking innovative ideas and developing them in alignment with organizational goals and strategy (Ramírez, Roodhart, & Manders, 2011). This group of people is arguably the most important innovation resource in the organization as they are often able to achieve innovation in the absence of well-defined innovation strategies or formally defined roles to direct innovation.  Unfortunately, in the absence of a strategic innovation practices, innovation success is less than assured, becoming the victim of conflicting responsibilities.

Instead of relying on happenstance, organizations should create specific job roles whose entire function is to surface, develop, and promote innovation ideas specific to one strategic organizational goal.  While the strategic goals themselves may change from year-to-year, or be longer term, these strategic innovation specialists are charged with all aspects of taking ideas aligned with their strategic focus from ideation all the way through market release.  These individuals are like innovation product managers, with a portfolio of potential innovation ideas.

The caveat here is that each individual (or team) should be focused solely on one strategic organizational goal, and must have the appropriate resources (outside of existing product management) to develop and mature their innovation portfolio, which leads us to the last group of people necessary for succesful innovation.

Innovation Leadership

Developing an innovation capability within an organization takes substantial effort (Barreto, 2010; Wilson & Doz, 2011).  Accumulating the vast knowledge resources to drive innovation and implementing the systems and processes to integrate knowledge into innovative execution takes significant resources, will, and commitment.   At the top of Figure 1, innovation leadership develops knowledge networks, provides resources to create innovation processes, and the creation, funding, and direction of strategic domain groups (Brown & Anthony, 2011; Engel & Del-Palacio, 2011; Ramírez et al., 2011; Rufat-Latre, Muller, & Jones, 2010).  Without dramatic changes in the way organizations are led, innovation cannot consistently take root (Hamel, 2009).

Innovative management strategies incorporate novel ways of interacting with customers, driving cultures of trust, and opening the organization to honest debate (Abele, 2011; Capozzi, Dye, & Howe, 2008; McGrath, 2011).  McGrath argues fear of failure inhibits organizations from achieving great innovation and an acceptance of potential failure can help organizations use failure to achieve success. Capozzi, Dye and Howe report the benefits of challenging the status quo of the organization often presents a springboard to innovation.  In much the same way reducing the fear of failure helps to spark responsible risk taking, reducing the fear of challenging organizational orthodoxies helps ensure that new ideas are not discarded simply because they are counter to the way things are currently done.  These cultural changes are more difficult than getting the right people or developing systems and processes; they require commitment at the highest levels of the organization.  Without leadership demonstrating this commitment to innovative practices, organizations are unlikely to truly capture their innovative capabilities.

Bridging the Divide

Having all the right people is critical to achieving long-term innovation capability.  Organizations already have an embarrassment of riches in terms of innovative, creative ideas, but without the appropriate people committed and dedicated to their development, innovation success is only a matter of chance.  It happens all the time, but rarely more than once or twice within the same organization.  Only the organizations designed and aligned to foster innovation and committed to the process achieve long-term, repeatable innovation success (think Shell, P&G, or 3M).  Simply telling your employees to be more innovative or offering a suggestion box is not sufficient.

Even having the right people is not enough.  Without the processes governing how these people work together to create successful innovation, success is possible but not guaranteed.  In the next Innovation Playbook, we will look at how to manage the innovation process for success. 

 

References

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

Barreto, I. (2010). Dynamic capabilities: A review of past research and an agenda for the future. Journal of Management, 36(1), 256–280. http://doi.org/10.1177/0149206309350776

Brown, B., & Anthony, S. D. (2011). How P&G tripled its innovation success rate. Harvard Business Review, 89(6), 64–72. Retrieved from http://hbr.org/

Buisson, B., & Silberzahn, P. (2010). Blue ocean or fast-second innovation? A four-breakthrough model to explain successful market domination. International Journal of Innovation Management, 14(3), 359–378. http://doi.org/10.1142/S1363919610002684

Burke, A., van Stel, A., & Thurik, R. (2010). Blue ocean vs. five forces. Harvard Business Review, 88(5), 28. Retrieved from http://hbr.org/

Capozzi, M. M., Dye, R., & Howe, A. (2008). Sparking creativity in teams: An executive’s guide. McKinsey & Company, (April 2011), 1–8. Retrieved from http://www.mckinsey.com

Choong, K. K. (2008). Intellectual capital: definitions, categorization and reporting models. Journal of Intellectual Capital, 9(4), 609–638. http://doi.org/10.1108/14691930810913186

Dell’Era, C., & Verganti, R. (2010). Collaborative strategies in design-intensive industries: Knowledge diversity and innovation. Long Range Planning, 43(1), 123–141. http://doi.org/10.1016/j.lrp.2009.10.006

Engel, J. S., & Del-Palacio, I. (2011). Global clusters of innovation: The case of Israel and Silicon Valley. California Management Review, 53(2), 27–49. http://doi.org/10.1525/cmr.2011.53.2.27

Kim, Y., & Ployhart, R. E. (2014). The effects of staffing and training on firm productivity and profit growth before, during, and after the Great Recession. The Journal of Applied Psychology, 99(3), 361–89. http://doi.org/10.1037/a0035408

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

 

Phelps, C. C. (2010). A longitudinal study of the influence of alliance network structure and composition on firm exploratory innovation. Academy of Management Journal, 53(4), 890–913. http://doi.org/10.5465/amj.2010.52814627

Ployhart, R. E., Nyberg, A. J., Reilly, G., & Maltarich, M. a. (2014). Human capital Is dead; Long live human capital resources! Journal of Management, 40(2), 371–398. http://doi.org/10.1177/0149206313512152

Ramírez, R., Roodhart, L., & Manders, W. (2011). How Shell’s domains link innovation and strategy. Long Range Planning, 44(4), 250–270. http://doi.org/10.1016/j.lrp.2011.04.003

Reeves, M., & Deimler, M. (2011). Adaptability: The new competitive advantage. Harvard Business Review, 89(7/8), 134–141. Retrieved from http://hbr.org/

Rufat-Latre, J., Muller, A., & Jones, D. (2010). Delivering on the promise of open innovation. Strategy & Leadership, 38(6), 23–28. http://doi.org/10.1108/10878571011088032

Rothaermel, F. T., & Hess, A. M. (2010). Innovation strategies combined. MIT Sloan Management Review, 51(3), 13–15. Retrieved from http://sloanreview.mit.edu/

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

 

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